In 2015, they scored original pieces for a 72-piece orchestra, an opera-dance project with Son Lux, and a smaller 30-piece string ensemble, while touring the US extensively. In June of 2016, they released a brand new, self-produced EP called Parking Lot, of which the single, "Michigan and Again," went viral. They've kicked off 2017 as major label artists, having signed a record deal with Sony Masterworks to release a full-length album in August.
Visit their website, or become a member of their FamGrove to be a part of the journey.


text:
\section{Introduction}
Electronic structure theory\cite{Helgaker2000,Parr1989}
is at the core of many theoretical and computational approaches in
the molecular sciences as well as in condensed matter and solid state physics.
The Born-Oppenheimer approximation \cite{,Tully2000}
introduces the electronic Schr\"odinger equation that treats all electrons of an atomistic system as dynamical
entities moving in the field of a fixed nuclear scaffold. The success of computational molecular and materials science
is rooted in this framework, and naturally, an ever growing number of electronic structure models has been devised in the
past decades that aim at improving on existing approximations in terms of general applicability,
increased accuracy, and optimized efficiency with respect to computational time.
As a good measure of these efforts may serve the rate of introduction, adoption, and use
of capital-letter acronyms that denote the various models and approximations.
In fact, their number has already grown so large that one wonders where this development will lead to in the future.
In this work, we assume a bird's eye view on the current status of developments
in an attempt to assess and determine options for future models.
Before we provide our perspective on these developments, we first dwell on the theoretical foundations
of electronic structure models.

\section{Theoretical Foundations}

The explicit decoupling of nuclear and electronic
degrees of freedom through the Born-Oppenheimer approximation
is, in many cases, an efficient and reliable approximation, but most importantly it can be systematically
improved to include nonadiabatic and rovibronic couplings \cite{Tully2000}.

The Born-Oppenheimer approximation introduces the concept of molecular structure as a spatial arrangement of nuclear positions,
making a local minimum structure the representative of a stable compound.
The emergence of (classical) molecular structure can be studied within a full quantum framework (see Refs.
\citenum{Woolley1976,Matyus2011,Matyus2011a,Muolo2019a}
and references therein),
but the application of such a general pre-Born-Oppenheimer approach for
calculations on large systems is unfeasible in practice. The reason for this is not only the prohibitive computational cost of
a truly accurate all-particle quantum calculation, but also
the lack of conceptual understanding when the concept of 'potential energy surface', i.e., the change of the electronic
energy with nuclear coordinates, is simply not available. Instead, in pre-Born-Oppenheimer quantum theory
we obtain only a spectrum of (molecular)
energy eigenvalues for the total system of electrons and nuclei without
any direct relation to molecular structure. For this reason, electronic structure theory in the Born-Oppenheimer
approximation has become the prevailing theoretical approach because it allows us to think in terms of classical molecular structure
and to improve on this approximation a posteriori in an efficient manner.

From the point of view of practical considerations and
numerical calculations, the Born-Oppenheimer approximation has become the key entry point for actual calculations of
molecules.
Its most decisive consequence is the assignment of an energy, the electronic energy, to a given molecular
structure -- which may be an equilibrium structure of a chemical compound or an arbitrarily distorted nuclear scaffold
-- by virtue of the emerging electronic Schr\"odinger equation.

The decisive difference of quantum chemical models compared to other types of atomistic modeling approaches -- such as force fields that govern the
interactions of atoms (as new emergent entities, rather than electrons and nuclei) -- is that the interaction of their dynamical objects, i.e., the electrons,
is well known and determined by the laws of quantum electrodynamics. If the speed of light is taken to be infinite, then the
interaction law will be known exactly and simply be given by the electrostatic Coulomb interaction of two point charges promoted to an operator,
whereas magnetic interactions and retardation effects vanish in this limit \cite{Reiher2015}. This advantage together with the fact that
any atomistic system is built of electrons (and, of course, of atomic nuclei) makes quantum chemical models universally
applicable. Hence, these models that rest on the first principles of quantum mechanics enjoy a specific status in theoretical
chemistry and molecular physics.

It is often taken for granted that successful electronic structure models can be applied to arbitrarily sized molecules (of course,
up to a point
where the size of the molecule will be in conflict with computational feasibility). In other words, if the size of a molecule under study
changes (e.g., by substitution for which benzene and toluene may be taken as an example), then we will still expect our computational
models to work if we start the computer program that calculates an approximation to the electronic energy.
Hence, an implicit requirement for the development of general approaches
that solve the electronic Schr\"odinger equation has been that they can
be applied efficiently for a fixed, but otherwise variable number of electrons; only the scaling behavior of an approximation
with system size will put some upper limit on this generality depending on actual computer hardware available.

Independent of available hardware is the fact that very accurate electronic structure
methods scale unfavorably with system size and therefore require a significant computational effort.
As a consequence, they can only be applied to far smaller systems than those accessible to more approximate approaches,
which trade accuracy for applicability.
Although system size is a moving target in view of continuously evolving
algorithmic improvements and hardware capabilities, the situation does not change in principle:
very large systems are approached with rather approximate
methods, whereas very accurate methods remain to be bound to very small molecules.

It is only for historical reasons that all electronic structure models have been first devised for the ('non-relativistic')
Schr\"odinger equation rather than for many-electron Hamiltonians based on Dirac's theory of the electron, which delivers
an equation of motion that is Lorentz covariant for a single electron in a classical electromagnetic field \cite{Reiher2015}. Considering the fact
that observables such as energies are the prime target quantity of computations,
the fundamental Einsteinean framework of special relativity
requiring Lorentz invariance of all fundamental physical equations has been reduced
to its numerical effect on observables obtained by relativistic quantum chemical methods.
For example, Taylor series expansion in powers of the inverse speed of light
has been considered early on and introduced new concepts such as spin-orbit, spin-spin, spin-other-orbit interactions and
kinematic relativistic effects. Although there exist convergence issues with such a Taylor series expansion,
it delivers well-defined analytical expressions for operators that can be used in perturbation theory to
correct the electronic energy for such effects. Naturally, efficient variational relativistic procedures have also been devised \cite{Reiher2015}.

Important for the assessment of errors of approximate relativistic energy
operator expressions is that the dependence of relativistic approximations on nuclear charge, electron kinematics, and spin
appears to be such that no
error accumulation or growing uncertainties result in
relative quantities such as reaction energies. This is due to the fact that relativistic effects are often
atomically conserved (e.g., during reaction) and therefore remain largely unchanged in chemical transformations.
This is also the reason why surrogate potentials that model relativistic atomic cores (e.g., effective core
potentials and pseudo potentials \cite{Dolg2012}) are very accurate in capturing relativistic effects (and even reduce the computational effort
through elimination of core electrons and alleviate the discretization error of a finite basis set through elimination
of the nodal structure of the wave function in the core region).

For what follows below, the most important consequence is that no (approximate) relativistic generalization will introduce an
error in the eletronic energy that is difficult to control when the underlying nuclear scaffold changes (e.g., during chemical
reaction). In other words, the change of electronic energy due to the principles of Einstein's theory of special relativity
is very well understood, up to the point where it has become possible to calculate the binding energy of molecular hydrogen
to an absolutely remarkable precision \cite{Puchalski2017,Puchalski2018,Puchalski2019,Wang2018}, in excellent agreement with high-precision spectroscopic measurements \cite{Cheng2018,Holsch2019}.
Effects that transgress the realm of quantum electrodynamics (such as electroweak effects \cite{Berger2000}) are known to
be orders of magnitude smaller.

\begin{center}
{\bf Statement 1:}
{\it
The theoretical foundations of electronic structure theory are very well established and understood.
}
\end{center}

Various well-defined relativistic generalization of the
standard non-relativistic electronic Schr\"odinger equation have been developed, which connect the quantum theory
for atomistic systems to the fundamental theory of quantum electrodynamics \cite{Reiher2015}. Since these relativistic variants do not pose
any unsurmountable hurdle to approximations designed for the efficient solution of the non-relativistic electronic Schr\"odinger equation,
electronic structure methods developed in the non-relativistic regime
apply equally well in the relativistic domain.
By contrast, electronic structure theory had to deal
with the peculiarities of many-electron systems in general, which are first and foremost the Coulomb interactions of the (point) particles
featuring a singularity where the particle coordinates coalesce.

\begin{center}
{\bf Statement 2:}
{\it
The quantum mechanical Coulomb interaction of two electrons is the crucial challenge for accurate approximations.
}
\end{center}

\section{Electronic Structure Models}

Two main classes of approximations have emerged in electronic structure theory: wave-function-based models
\cite{Helgaker2000} and density functional theory (DFT) \cite{Parr1989}, with variants that
bridge both classes. In actual calculations, DFT is often applied as a parametrized model because of
approximations in the exchange-correlation energy functional that require parameters, in most cases determined for some
reference data.
Despite the universal nature of these parameters,
which are taken to be valid for any sort of atomistic system,
and despite the first-principles nature of DFT per se, actual applications are sometimes classified as 'semi-empirical' refering
to the parameter dependence on the reference (training) data and to the to a certain degree arbitrary assumptions on the analytical form of
a functional.
Proposals to improve on this situation have been numerous,
but the general dilemma of electronic structure theory, i.e., the ineluctability to
compromise accuracy for computational efficiency also hampers these attempts. This is already seen in the standard
approximations of DFT, which Perdew ranked as a Jacob's ladder \cite{Perdew2001} (a hierarchical ranking that has been confirmed
in rigorous and extensive numerical studies \cite{Mardirossian2014a,Mardirossian2017,Mardirossian2018}), for which improved accuracy comes at significantly increased computational cost.

Although the existence of the exact exchange-correlation energy functional has been proven \cite{Hohenberg1964,Lieb1983},
its exact analytical form is and most likely will remain unknown. In view of the increasing complexity of advanced density
functionals \cite{Becke2014}, one is struck by the impression that the exact exchange-correlation functional (or a very accurate approximation
to it) will be as hard to evaluate as a wave-function model of the same accuracy.

By contrast, wave function theories often allow for systematic improvement owing to a systematic and well-controllable
construction of the exact electronic wave function through basis-set expanion techniques -- at least in principle, but
not to an arbitrary degree in practice. In a mathematically stringent way, the electronic wave function can be
expanded in terms of a complete many-electron basis set. The
many-electron basis functions can be systematically built (usually as determinants) from products of one-electron functions
(e.g., Hartree-Fock orbitals or some general type of energy-optimizing orbitals),
which themselves are generated as a linear combination of pre-defined and therefore known one-electron basis functions
(in the notorious linear combination of atomic orbitals).

All standard models -- such as
configuration interaction, coupled cluster (CC), complete active space self-consistent field, or M{\o}ller-Plesset perturbation theory --
then approximate the electronic wave function by a superposition of these many-electron basis functions, of which an ordered
set is
generated and classified according to orbital substitutions compared to a (dominant) reference determinant
(so-called 'excitations'). Although this choice of singles (S), doubles (D),
triples (T), ... excitations for the definition of classes of many-electron basis functions
produces a well-defined approximation hierarchy, it does, a priori,
not guarantee optimum convergence as it is not clear which
'excitations' will be decisive for the model to achieve a given accuracy. However, as long as changes in molecular structure
are local (as is the breaking of chemical bonds in a reaction mechanism), very high excitations may be avoided.
A vast body of numerical results has therefore led to the wide acceptance of the CCSD(T) coupled cluster model \cite{Raghavachari1989}
as a 'gold standard' for quantum chemical
calculations. It is, however, important to emphasize that although this approach can deliver results
of chemical accuracy of about 1 kcal/mol in benchmark calculations,
the precise accuracy of the approach for some given case is, in general, not known. Instead, one resorts to ensure some general
constraints on the electronic structure of a molecule under investigation that must be fulfilled in order
to achieve a high accuracy with the CCSD(T) model. These constraints require a (close to) equilibrium molecular structure, a closed-shell
electronic structure, absence of multi-reference effects that are typical for open shells and so forth.

Unfortunately, no such model of similar reliability, but universal applicability could be established for the
general multi-configurational case. Although
many open-shell and bond-breaking situations may be well captured by ground-state coupled cluster theory with
a not too high degree of excitations in the electronic wave function,
it is troublesome that the most general case has escaped attempts to produce an
equally accurate and broadly applicable electronic structure model.
Surely, modern complete-active-space approaches such as the density matrix renormalization group (DMRG) \cite{White1992,White1993,Baiardi2020} or full configuration
interaction quantum Monte Carlo (FCIQMC) \cite{Booth2009,Ghanem2019,Ghanem2020} have pushed the limits to a degree where any (for reaction chemistry) relevant active orbital
space can be treated (i.e., of up to one hundred active spatial orbitals), the neglected dynamic correlation from the huge number of weakly-correlated
orbitals, which are outside the active space, escapes a treatment on the same level of accuracy; typically
only multi-reference perturbation theory to second order represents the highest level of accuracy achievable.
Continuous efforts have tried to improve on this situation; examples are
tailored coupled cluster \cite{Bartlett2005,Veis2016,Morchen2020},
combinations with short-range DFT \cite{Savin1996,Savin1997,Jensen2007,Hedegard2015,Jensen2018,Rodriguez-Jimenez2021,Pernal2021}
or transcorrelation \cite{Boys1969,Shiozaki2012,Luo2018,Baiardi2020a,Guther2021}
to treat the electron-electron cusp due to the singularity of the Coulomb interaction,
to mention only a few.
Already the amount of suggestions for accurate multi-configurational methods (not reflected in the list of references of this work
and beyond its scope) may be taken as an indication
that no universally satisfactory and generally accepted best solution has been found so far.
At the same time, multi-reference coupled cluster theory has suffered from formal difficulties \cite{Evangelista2018}, by which each of the various
ans\"atze is plagued to a different degree.
It is therefore no surprise that other advanced CI methods have seen developed in recent years
\cite{Umrigar2016,Whaley2016,Evangelista2016,Zimmerman2017,Zimmerman2017a,Eriksen2018,Loos2018,Chilkuri2021,Chilkuri2021a}
as a potential way to approach full configuration interaction (FCI) results.

Hence, one may doubt whether this situation can in principle be improved on, and a quantum leap to solve these principal problems,
that will also need to stand scrutiny in terms of computational feasibility, appears not very likely on traditional computers.
Naturally, many have wondered whether there are other, better suited approaches that await a discovery or
just need to flourish \cite{Popelier2011}. Hence, alternative approaches have been sought for, but all developments rely on
more or less the same principles and technologies and still
wait for an eventual breakthrough. Even if this happens in the future, it is likely that there will
always be strings attached that will eventually restrict the range of applicability of future methods in one way or another.

The most dramatic improvement may come from hardware development in the future,
of which the most prominent example is the hope to solve electronic structure problems with future quantum hardware
\cite{Abrams1997,Abrams1999,AspuruGuzik2005,McArdle2020,Liu2021},
which may deliver FCI-quality results up to a pre-defined accuracy by quantum phase estimation (QPE). Although current implementations
rely mostly on the variational quantum eigensolver \cite{Peruzzo2014}, QPE can be shown to be feasible on moderately sized future quantum
computers for actual chemical (rather than toy) problems \cite{Reiher2017,vonBurg2021}.
In this context, it is important to understand that a typical electronic structure problem represented
in an orbital basis of, say, 1000 one-electron basis functions, will require 2000 logical qubits to represent the
state (which, in turn, will require orders of magnitude more physical qubits for error correction purposes), let alone the
problems of initial state preparation and loading the Hamiltonian parametrization in second quantized form into
the machine (consider, in particular,
the two-electron integrals for an active space of 1000 orbitals). However, if such large universal digital quantum
computers become
available in the future and if they are widely accessible,
then they can produce results of FCI quality for molecular sizes that can accurately describe
any local reactivity event, which will render very many traditional electronic structure models superfluous
(for instance, the separation into static and dynamic correlations may become unnecessary).

As a side remark, if one is willing to cool down a quantum computer of sufficient size
to very low temperature, then such a strategy would also be beneficial for traditional approaches towards cryogenic computing.

For the reasoning in this paper, we adopt the point of view that
we will have to cope with the situation as it presents itself to us today
in the near and maybe also in the distant future. Namely,
that we will accomplish incremental improvements in all directions, but that no generalist approach of arbitrary accuracy
for some system under study is likely to emerge.

\begin{center}
{\bf Hypothesis 1:}
{\it
 The current situation of electronic-structure models might represent the end point of our efforts to
devise universally applicable, accurate, and feasible electronic structure models. In view of
the tricks and techniques used in all developments, it appears to be unlikely that paradigm-shifting new ideas will emerge
in the (near) future.
}
\end{center}

Even if this hypothesis will be falsified in the future, it is the best starting point for a discussion on how
to deal best with our current arsenal of electronic-structure methods for solving pressing chemical problems.

\section{The Role of One-Electron Basis Sets}

It is hard to obtain accurate quantities for many-electron problems. Already
the choice of a typical one-electron basis set usually affects the total electronic energy
significantly, because basis sets are designed for specific purposes and compromise on others for the sake of
computational feasibility. For instance, to allow for sufficiently accurate
results of relative electronic energies for chemical reactions, 'atomic orbital' basis sets attempt to represent the
valence-shell region well
by suitable polarization and diffuse functions. The core shells, i.e., those molecular orbitals that appear like
atomic $1s$-type orbitals, are not very well represented, which affects their orbital energy. If one expresses the
total electronic energy as a function of orbital energies (e.g., as the sum of all canonical orbital energies plus the one-electron
integrals in Hartree-Fock theory divided by two) then it is obvious that the large core-shell orbital energies affected by
a large error propagate this error to the total electronic energy. However, for relative electronic energies of processes where
atomically conserved features of the electronic structure are unimportant, these errors will drop out as they are conserved
in reactants and products.

Apart from the electronic energy, also calculations of its derivatives -- which define molecular properties through
response theory \cite{Norman2018} and can be taken as 'relative quantities' by definition (as is particularly obvious in the finite-field
approach) -- usually factor in error compensation, but may require special functions to represent
a property well. For instance, polarizabilities naturally require one to allow for polarization effects, and hence,
polarization functions will be very important \cite{Sadlej1988}. Taking further derivatives, e.g., for the calculation of
Raman and Raman Optical Activity spectra has led to the proposal of tiny skeleton basis sets with polarization functions
to reduce the computational effort \cite{Zuber2004} which turn out to be quite accurate for such 'relative' quantities \cite{Reiher2005a}.

The singularity at the coalescence point of the electron-electron Coulomb interaction will require extended
one-electron basis sets, especially for correlated electronic structure methods. To achieve chemical accuracy with decent triple-zeta
basis sets requires the introduction of explicitly correlated functions, as elaborated into the now established F12 ansatz
\cite{Ma2018}, or a modification of the Hamiltonian as in the transcorrelation approach \cite{Boys1969} or through range separation
by short-range DFT \cite{Savin1996}.

In general, even large orbital basis sets therefore suffer from errors that are well above 1 milliHartree, an accuracy that nevertheless
may be achieved for {\it relative} quantities due to the fortunate error compensation of atomically conserved parts of electronic
structures. While this observation has been central to the application of electronic structure models in computational quantum chemistry,
rigorous mathematical results on the discretization error introduced by finite basis sets are still needed (see Ref. \citenum{Cances2017a}).

Large standard valence-optimized one-electron basis sets
will not allow one to reach a high accuracy in the total electronic energy of, say, 1 nanoHartree,
for which one is advised to switch to explicitly correlated basis functions such as geminals \cite{Adamowicz2013,Matyus2012}.
Depending on the target problem, such an accuracy might be needed. While kinetic details of a reaction will require one to
know the relative electronic energy to an accuracy better than 1 kJ/mol, which is somewhere inbetween milliHartree and microHartree
accuracy, nanoHartree accuracy may become important for high-resolution spectroscopy, of which the measurement of the
H$_2$ binding energy is a prime example \cite{Puchalski2019,Wang2018,Cheng2018,Holsch2019}.
It is therefore obvious that {\it the required target accuracy} needs
to be factored into the effort one spends on solving the electronic structure problem.

\begin{center}
{\bf Statement 3:}
{\it
 While the discretization error introduced by orbital and geminal basis sets -- as well as any other error that results
from the technical implementation of solution procedures -- may be controlled and systematically reduced, its
value in an actual calculation is usually not precisely known.
}
\end{center}

\section{Can We Know the Error by Which a Specific Quantum Chemical Result is Affected?}

The Ritz variational principle provides us with a means to judge on the reliability of total electronic energies
obtained with approximate (variational) electronic structure models by assuring that they are
upper bounds to the ground-state energy eigenvalue of the electronic Hamiltonian.
As a consquence, we know that the lowest approximate energy represents the best approximation to the exact total electronic
energy. To assess the remaining error requires, however, a lower bound that can be efficiently computed. Despite continuous development
devoted to establishing such rigorous lower bounds for Hermitian operators
(see Refs. \citenum{Cances2017,Cances2018,Martinazzo2020a} and references therein), this ansatz remains underexplored.
Moreover, even if such a lower bound can be efficiently computed, this will not imply that the
uncertainty obtained as the difference between both bounds is sufficiently small to allow for rigorous
error assessment.

Analytical error assignment based on the structure of the underlying differential equations (i.e., the electronic
Schr\"odinger equation or its descendants, the self-consistent field equations) is very hard as well.
At the level of total electronic states,
the external potential created by the nuclear scaffold presents a complicated variety as a coefficient function in
the partial differential equations; i.e., slightly shifted nuclear positions create a shifted energy eigenvalue
spectrum easily producing different electron correlation patterns that give rise to emerging forbidden crossings or
conical intersections \cite{Domcke2011}. At the orbital level, the fact that the electron-electron interaction requires knowledge of the
solution, which is dealt with by the self-consistent field procedure, makes the corresponding one-electron partial differential
equations hard to analyze.

A change in the external potential (e.g., as it occurs along a reaction coordinate) is continuous and
typically changes the error of an electronic structure model in a smooth and continuous, non-abrupt fashion (even if this error may,
successively, become extremely large as, for instance, in coupled cluster calculations of at most doubles excitations in
double-bond breaking processes). As such, the error is systematic and, obviously, does not depend on any sort of noise
(as long as the computer hardware is certified). However,
this usually does not allow one to explicitly know the error in a specific quantum chemical calculation
without carrying out an improved calculation. Unfortunately, the latter is, for various reasons, typically unfeasible
(otherwise one would have chosen the more accurate (and, hence, more expensive) calculation from the start).

\begin{center}
{\bf Statement 4:}
{\it
The individual absolute error of a specific quantum chemical result is usually very hard,
if not impossible, to assess accurately.
}
\end{center}

\section{Systematic Improveability}

For the purpose of this work, it is instructive to now look at electronic structure methods from the point
of view of how errors introduced by various approximations are actually assessed in routine calculations.

It is the specific nature of first-principles methods to be well-defined in terms of the fundamental
interactions requiring hardly any model assumptions on the interaction operators -- other than those that emerge from quantum electrodynamics.
This formally convenient and intelectually appealing situation has given rise to the romantic belief that a
general electronic structure model -- applicable to any atomistic system with sufficient accuracy
(say, with chemical accuracy of at least 1 kcal/mol) -- can actually be devised if we only search for it with sufficient
devotion and dedication. Because of this attitude that we may somehow be able to approximate the exact solution of the electronic
Schr\"odinger equation with sufficient accuracy, the modeling of electronic structure has hardly been scrutinized by
rigorous uncertainty quantification procedures applied routinely to a specific atomistic system under investigation.

It is natural for many-particle basis-set expansion approaches such as configuration interaction and coupled cluster that one
can approximate the exact solution of the electronic Schr\"odinger equation to arbitrarily high accuracy under the provision
(i) that one ignores the exponentially scaling wall due to the exploding number of many-electron basis functions
with system size (even when acknowledging that there may be a lot of deadwood in $N$-particle Hilbert space that
might be avoided by smart procedures, such as DMRG and FCIQMC mentioned above)
and (ii) that one has chosen some suitable finite one-electron (orbital) basis set in which an accurate solution can
be obtained. Following the excitation hierarchy in these schemes allows one to systematically
approach a 'complete' (finite) many-particle basis set and, hence, the full configuration interaction
energy in this basis.

Interestingly, this idea of systematical improvability is hardly pushed to the limit in practical calculations,
where one prefers to pick just one well-established generalist model (such as CCSD(T)) to have only one calculation
to rely on so that one does not drown in numerical data, can focus on the scientific question to be answered, and does
not spend additional computational time on it. However, in such cases it is not at all clear how reliable the calculated data
are at all for answering a chemical question (regarding, for instance, the elucidation of mechanistic pathways).
While the prevailing assumption is that CCSD(T) combined with a suitable one-particle basis set will be sufficiently
accurate, this situation actually points toward the need for system-specific
error assignment with little additional computational effort.

Pople and co-workers ordered wave-function-theory models according to their expected accuracy, in order to establish
extrapolation schemes that allow one to exploit orbital bases of different size to make the high-excitation-degree wave functions
accessible, leading to combination theories such as G2 \cite{Curtiss1991,Curtiss1992,Curtiss1995} and G3 \cite{Curtiss1998,Curtiss1999,Curtiss1999a}. Such composite models have been further developed by other groups
(see, for instance, Refs. \citenum{Martin1999,Boese2004,DeYonker2006,Karton2006,Barnes2009,Das2010,Alsunaidi2016,Karton2017} to mention only a few).
At the same time, the result of such approaches is still
affected by an error, even if it may be significantly reduced, which is not known precisely for a specific system under study.

In DFT, the situation is more cumbersome because of the more or less empirical introduction of parameters and analytical functional forms that define
the approximate exchange-correlation energy functionals. They
do not allow for a systematic improvement as one has no idea about what to change in order to
arrive at a more accurate answer. In other words, having obtained a DFT result
with some density functional, by and large, presents us with the problem that it is not clear
how to improve on it or even how to scrutinize it. Surely, climbing up
the notorious Jacob's ladder \cite{Perdew2001} could be one option, but this concept is more heuristic than rigoros at its core.
In particular, improved results can only be expected in an average manner \cite{Mardirossian2017}, whereas a specific result for a
specific system may not improve even if one takes the next rung of the ladder.

\begin{center}
{\bf Observation:}
{\it
 Electronic structure models are affected by some error that is usually not assessed in a specific application.
Instead one often relies on experience and intuition gained with some approach.
Most approximations rely on error compensation that typically leads to a more reliable results for
relative quantities, but their precise accuracy for a specific case under study is typically not known either.
}
\end{center}

While one may quote very many examples for this observation,
which would be far beyond the scope and purpose of this
perspective, we may instead take it as a stepping stone in our gedankenexperiment that weighs in on the future
of electronic structure methods. We may therefore continue our discussion from this conclusion:

\begin{center}
{\bf Conclusion 1:}
{\it
 While we have a very good understanding of what approximations in electronic structure theory
are feasible and efficient, they are, in all cases,
affected by an error that will be unknown for a specific molecular structure under consideration, even if this
error is expected to be small for certain approaches.
}
\end{center}

\section{Traditional Error Assessment Strategies}

Rigorous error estimation approaches based on reference results are not without difficulties.
The generalist approach of electronic structure has relied on representative
benchmarking, where reference or training data have been selected based on
chemical intuition or simply based on availability.
As a result, transferability -- usually implicitly assumed if mean absolute deviations are found to be sufficiently
small for training or test data sets -- may not come without accepting the fact
that one may encounter large unexpected errors. Large absolut deviations
obtained for reference data might differ significantly from the mean
absolute deviation, which may be taken as a first hint of compromised transferability.
However, these standard measures
can only provide indications
about errors to be expected for test data if the reference data can be shown to be related in some way to the
test data. The latter condition is not trivial to demonstrate and certainly not a question of experience
or chemical intuition.
Moreover, only few more sophisticated ways have been proposed to improve on the stochastic insight gained for
benchmarking against a specific training data set \cite{Proppe2017a,Pernot2017,Pernot2017a,Weymuth2018,Pernot2018,Pernot2020}.

A major problem has been that, historically, reference data sets have not been rigorously
developed based on stringent stochastic criteria, but often emerged
as being determined by availability of reliable data rather than by demand of specific data.
The general situation improved in the new millenium,
but accurate reference data is still lacking for challenging problems such as transition metal complexes
or (very) large molecules. Despite meritorious efforts to enlarge these reference sets (see, for example, Refs.
\citenum{Korth2009,Goerigk2010,Goerigk2017,Mardirossian2017,Dohm2018})
in order to cover typical chemical phenomena with data points that appear to be reasonable, the problem remains that
they are usually not scrutinized by standard statistical means. It is for this reason that we have begun \cite{Proppe2017a,Simm2017b}
to intoduce basic standard tools such as bootstrapping \cite{Efron1979} and jackknifing \cite{Miller1974} to elucidate confidence intervals and the importance
of individual data points, respectively. With these tools, we were able to demonstrate \cite{Weymuth2018}, for instance, that the error
development of an increasing number of pairwise semiclassical dispersion corrections may not necessarily lead
to error compensation, but instead to error accumulation.
These findings cast some doubt on the general transferability of such corrections to large molecular systems,
whose size transgresses that of those molecules for which the corrections were parametrized.

More importantly, results obtained for benchmark sets suffer from the fact that one does not know to what degree they
are actually transferable to an actual case at hand.
Surely, how severe this transferability issue is will also depend
on the required target accuracy in such a way that a tolerable low accuracy may alleviate transferability concerns.
Still, one will require some objective measure by which the actual accuracy can be assessed or, at least, estimated.

Transferability of a given electronic structure model to a specific new atomistic problem is
not guaranteed and unexpectedly large errors can be encountered, often without warning (for examples see
Refs. \citenum{Reiher2001,Salomon2002,Cohen2012,Weymuth2014,Liu2015,Husch2018}).
These encounters of unexpected outliers may be taken as an indication that
reference data are likely to be incomplete if chemical accuracy shall be reached for arbitrary systems
and point to the need of continuous benchmarking monitored by an uncertainty quantification protocol.
It is even questionable whether we will ever have enough benchmark data at our disposal that allow us to show that
transferability to unseen molecular systems holds true in a way that a certain small error margin is not exceeded,
ignoring the fact that this would also require one to define a reliable measure of transferability.

\begin{center}
{\bf Corollary:}
{\it
Transferability of benchmark results to a specific case under investigation cannot be guaranteed in a rigorous manner.
}
\end{center}

\section{Bayesian Uncertainty Quantification}

A crucial conclusion for and from numerical studies
with various electronic structure models is that the exact error of a given model
for a specific molecule is not known. For instance, if the largest absolute deviation obtained for a model on some benchmark data
turns out to be much larger than what would be tolerable for drawing sufficiently reliable conclusions from the calculated
data, no guarantee can be given
that calculated results will be sufficiently reliable for the specific problem. For most electronic structure
models this actually implies that system-specific uncertainty quantification will be imperative, if predictive work
shall be accomplished -- even if chemical accuracy, which does not pose a very tight constraint compared to spectroscopic
accuracy, tolerates certain errors.
If one follows this line of thought, the next question is, how uncertainty quantification can be conducted in a way that
is reliable and does not come with a computationally prohibitive overhead.

In a theory proposed by Sethna and co-workers \cite{Mortensen2005} for DFT calculations, the sampling error of a class of
functionals is mapped onto the real error at a benchmark data set in such a way that one can exploit the sampling of calculated
results for error estimates for systems unrelated to the benchmark set. Unfortunately, the mapping affects only the linear
parameters in a functional \cite{Simm2016}. Nevertheless, this Bayesian approach that transfers knowledge obtained for benchmark
data to unseen molecular structures led to the development
of Bayesian error estimation functionals \cite{Wellendorff2012,Wellendorff2014}. Unfortunately, it turns out that mean and largest absolute deviations
remain comparatively large for this class of functionals \cite{Wellendorff2012,Wellendorff2014}. In principle, a system-focused approach
that re-adjusts the non-linear parameters to system-specific benchmark data achieves a significantly increased accuracy \cite{Simm2016}.
Still, even for such a system-specific
optimization of a state-of-the-art density functional the error estimation turns out to be not flawless on the training data,
i.e., it does not reproduce the error bars in all cases \cite{Simm2016}.
Hence, even a system-specific approach suffers from the fact that the analytical form of the density functional is not flexible
enough. Another issue with this methodology is that it
requires extensive DFT recalculations when benchmark data is extended for a given functional.

However, notorious machine learning models may step in and provide an alternative approach with various benefits.
For one, they can provide
a glue between data of different origin. Hence, they can connect data obtained with a fast-but-inaccurate model with those of
an accurate, but computationally more expensive model. Moreover, basically all machine learning schemes come with built-in uncertainty quantification
that allows one to rate the results obtained. However, since they are data driven, they rely on reference data, for whose production
provisions must be taken (see below).
As such, it is Bayesian in its very nature and therefore not easy to accept from a puristic first-principles point of view,
in which one may approach the exact solution to a problem -- at least in principle.

For example, we have demonstrated how to exploit machine learning
to improve on semiclassical dispersion corrections
(in this case by Gaussian process regression, GPR)
\cite{Proppe2019a} and on a classical force field \cite{Brunken2020}. In these cases, the existing model remains intact and
the machine learning approach corrects for deficiencies in this fixed model. The idea behind this procedure is
that the physical model may capture much of the physics of the problem, leaving only deficiencies for the machine learning model
to learn and correct so that, ideally, less reference data will be required in such a $\Delta$-machine learning \cite{Ramakrishnan2015} type of approach.

Machine learning models are data driven, but agnostic with respect to the type of data. As an example, we note that
GPR has been successfully applied to exploit system-specific information on confidence intervals for the
optimization of molecular structures \cite{Raggi2020}.

We also exploited machine learning (again we chose GPR) to assess the accuracy of approximate electronic
structure models deployed in the elucidation of large amounts of related molecular structures (e.g., encountered in
reaction-network \cite{Sameera2016,Vazquez2018,Dewyer2018,Simm2019,Unsleber2020} and
chemical-space exploration \cite{Shoichet2004,Pyzer-Knapp2015} or first-principles molecular dynamics \cite{Marx2009})
by comparing to more accurate reference
data. Here, the key advantage of GPR is to determine for which structure a reference calculation is required. This is encoded in
its kernel that requires a measure of molecular similarity, for which many are available such as the Coulomb matrix \cite{Rupp2012}
and the smooth overlap of atomic positions \cite{Bartok2013} (however, more work on such measures will be required as they need to become a rigorous measure for
relating molecular structures that will be affected by similar errors in the approximate method).
Such a procedure has two key advantages: It is computationally cheap to backtrack new benchmark data which were determined
by the machine learning model to be calculated, and it is reference-data-economic as such data is only demanded where needed
in a rigorous, objective fashion whose accuracy only depends on the choice of the kernel.
At first sight, this seems to come with the disadvantage that reference data must be provided on demand. However,
coupled cluster calculations can be easily launched for single-reference cases, and even multi-configurational
calculations can be launched in a fully automated fashion \cite{Stein2016,Stein2016a,Stein2017,Stein2019}.
To obtain accurate electronic structures and energies for molecules of increasing size, one may resort to embedding
approaches (see Refs. \citenum{Manby2012a,Muhlbach2018,Ye2020} and references therein) so that the quantum region which must be accurately described remains
to be of constant size.

It is important to appreciate that machine learning approaches can also take into account the residual error in the reference data
(see, e.g., Ref. \citenum{Proppe2019} for an application). Clearly, this only shifts the whole error-assessment problem to the model of higher accuracy:
it will require system-specific error estimation for the accurate reference, which will become increasingly
difficult (because the reference will typically be a model of high accuracy for which it is hard to improve on) and
therefore some sort of {\it error archegenesis} will be inevitable. As long as this is not possible based on the structure of the underlying
partial differential equations, a way out might be an incremental scheme in which long-range correlations are sampled.
Here, it is implicitly assumed that the more reliable the error assessment shall be, the more demanding this will become in terms
of computational effort. It is for this reason, that a hierarchical deployment of electronic structure models
of different degree of reliability is a reasonable way to proceed in order to accomplish reliable error estimation
in a systematic hierarchical manner.

\begin{center}
{\bf Conclusion 2:}
{\it
 Bayesian uncertainty quantification is a way out of the error-assessment problem,
but requires continuous benchmarking -- ideally with error assignment for the reference data themselves --
adjusted to the specific systems under study and selected on the basis of confidence intervals of
the underlying machine learning model;
its accuracy will depend on the measure with which one strides across
the parameter space (e.g., the space of all molecular structures defined by nuclear scaffolds that parametrize the Born-Oppenheimer surface).
}
\end{center}

\section{The Fate of Electronic Structure Models}

The historical development of electronic structure models has delivered many different classes of approximations with
different advantages and hence different areas of applications where they can blossom. Among these classes, variations
appear to be numerous and are typically characterized by capital-letter acronyms. Whereas only some of them are designed to
clearly replace older versions, most acronyms never die and are likely to survive in the future. The fact that very many
physical effects need to be approximated to eventually allow for a direct comparison with experiment (such as increasing molecular
sizes and complex environments (solvents, surfaces, enzymes, zeolites, metal organic frameworks, ...))
 together with the desire to propose new, potentially more successful methods has led
to an ever increasing number of acronyms. Already now, there are so many of them that it is even difficult for
experts to keep track and one wonders where this will lead to in a few decades because we may get lost
in a plethora of acronyms for variations of methods that may eventually choke scientific progress.

By contrast, the discussion in the last section points to the possibility to stay with
a sufficiently reliable approximation of a generalist electronic structure model and improve on it by selective and
directed refinement through system-specific benchmarking. Such a Bayesian approach supplements any approximate model with uncertainty
quantification for cases where no reference data is available and may even produce a system-focused variant of increased
accuracy. The system-specific uncertainty quantification alleviates the problem of
questionable or unknown transferability of traditional benchmark sets and eventually allows us to make
better founded predictions on chemical processes.

This is clearly a compelling path forward, but it might render many models superfluous. For instance, if electronic energies
and molecular structures can be predicted with uncertainties assigned, only one rather than one hundred variations of some
type of approximation will be sufficient (such as one density functional at a given rung of Jacob's ladder
or even one for the whole ladder, for which a computationally efficient one can be chosen).
This consequence is in accord with the conclusions drawn from the massive search for functionals by
Mardirossian and Head-Gordon \cite{Mardirossian2014a,Mardirossian2017,Mardirossian2018} who showed that not a single
unique functional would emerge, but very many functionals that differ only slightly in terms of accuracy measured at a large reference
data set. A slight change of this reference data set would then favor a different functional out of the pool of well-performing ones.

\begin{center}
{\bf Conclusion 3:}
{\it
One needs to know how accurate a computed result will be for a specific application and Bayesian
error estimation can be the key to provide this information, also weeding models of similar type and accuracy, hence
reducing the number of models that will be required.
}
\end{center}

\section{System-Focused First-Principles Models}

Consider the idea that benchmark sets will, in general, never be large enough for transferable parametrizations that are reliable
(even if a parametrized first-principles model features a high degree of flexibility).
Hence, reliable uncertainty quantification for a specific new molecular structure will uncover
features that are not well represented in the already existing benchmark data
-- based on a suitable measure for molecular and electronic similarity that is key for the transferability assessment of already existing data.
Then, continuous benchmarking (i.e., benchmarking upon request) will be the only way out if results shall be supplemented by
reliable Bayesian error
estimates. However, this comes with the benefit that one may re-adjust model parameters for a specific molecular
system to increase the accuracy of the model in the local region of chemical space assigned by the similarity measure.
Surely, one is advised to restrict such a reparametrization to values that were determined in the vicinity
of the starting values, provided that they define a suitable generalist model, in order to avoid a skew model that might not even
be transferable within a set of very similar molecular structures.

Hence, such a system-focused re-parametrization can increase the accuracy of an approximate electronic structure model
in such a way that a subsequent $\Delta$-machine learning is likely to require less reference data because it may
be anticipated that it will be easier to learn differential rather than absolute effects, if the physical model
is suitable and flexible and if reference data are limited.

A system-focused model may be developed in the neighborhood of a well-established one.
Whether or not such a procedure is worthwhile will depend on the type of application. If only few calculations
are to be carried out, one is advised to choose a model that may be considered to have generally small errors (such as
a coupled cluster variant). If, however, a massive computational campaign (as in reaction network explorations \cite{Sameera2016,Vazquez2018,Dewyer2018,Simm2019,Unsleber2020}
first-principles molecular dynamics studies \cite{Marx2009}, or high-throughput quantum chemical screening calculations \cite{Shoichet2004,Pyzer-Knapp2015}) is part
of solving a scientific problem, then a delicate balance of fast methods equipped with uncertainty quantification
through automated benchmarking upon request will be required. In order to limit the number of computationally expensive
calculations needed for the continuous production of reference data, one may exploit existing data of such a campaign to
refine the fast, but inaccurate model in a system-focused manner, as shown in Ref. \citenum{Brunken2020,Brunken2021}.

If system-focused reparametrizations are carried out in the neighborhood of existing electronic structure models,
more accurate ab initio reference data can be used for this purpose so that the procedure does not introduce
additional semi-empiricism, but may result in a potential loss of generality of the parent model.
However, this is made up for by the simultaneous assignment of uncertainty, for instance, through a machine leanring model such as GPR or
a neural network representation that mediates between the fast-approximate results and the fewer expensive-accurate reference
points. Naturally, it will be beneficial to collect expensive reference data of known high accuracy in globally
accessible data bases for permanent re-use.

Criteria for a successful exploitation of system-specific readjustment of parameters to decrease uncertainties
are that (1) reparametrization must be fast (i.e., no computational bottleneck must be created),
(2) it needs to be carried out autonomously (i.e., without requiring any human
interference or input) to be practical, and
(3) autonomy of reparametrization procedures requires full automatism of all calculation steps, especially for the
automated launch of reference calculations, which may require multi-configurational approaches.

\begin{center}
{\bf Conclusion 4:}
{\it
 Generalist electronic structure models cannot be accurate and fast at the same time (for large molecules or
molecular aggregates). A significant gain in computational
efficiency requires approximations that compromise transferability and accuracy. System-focused models
can be made fast and accurate, but their transferability, i.e., their accuracy for related
structures, must be monitored
because they have not been tested on some arbitrary reference data.
However, such reference data will be incomplete anyways if scrutinized with a suitable measure of molecular and electronic
similarity.
Having system-focused models requires autonomy regarding parametrization and uncertainty quantification, and therefore,
efficient automated procedures are needed in order to be practical.
}
\end{center}

\section{Outlook}

To know the uncertainty of a numerical result obtained from an approximate electronic structure model
is key for assessing the reliability and usefulness of a computational prediction.
It is risky, if not unacceptable, to rely on standard statistical measures such as the mean absolute deviation obtained for
some reference data whose relation to the case under study is not clear in a quantitative sense.

Rigorous Bayesian uncertainty quantification measures have been developed, but
having error estimation in place comes with an additional computational burden. Apart from the uncertainty quantification procedure
itself, a significant portion of the extra effort is due to the necessity of (continuous) benchmarking on demand. Still, this
should be embraced given that running reference calculations continuously is a way to produce system-specific error estimates in a well-defined objective manner
and a way to get rid of numerous variations of computational results for the same quantity
owing to the ever increasing number of electronic structure models.
Critical for the reference data generated is that also the uncertainty of the reference data is assessed and fed into the
overall error estimation process. Eventually, such a nesting procedure of methods ranging from fast, but rather inaccurate
to accurate, but computationally expensive approaches with decreasing uncertainty in the calculated result obtained for a specific
molecular system will require some sort of error archigenesis that estimates the error still immanet
in the most accurate approach available.

It is obvious that the accuracy of a calculated electronic structure result will not be sufficient for comparison with experiment.
Additional effects need to be taken into account, most importantly those of molecular structure. In other words,
nuclear motion and nuclear coordinates will affect the result that is eventually to be compared with an experimentally
measured quantity. Clearly, any additional computatinal modeling step will then also require rigorous uncertainty
quantification. As an example we refer to the role of nuclear coordinates in spectroscopy that introduces uncertainties
in the spectra discussed in Refs. \citenum{Oung2018,Bergmann2020}. A key challenge, that is likely to remain to a certain degree
at the discretion of the person conducting a computational study -- especially for large, nano-scale, composite atomistic systems --
is the choice of the atomistic configuration as a whole. If a selected atomistic structure does not represent the problem well,
no reliable uncertainty quantification will be able to detect this and the calculated results, even when equipped with
rigorous error estimates, will not at all relate to the experimental situation (the assignment of protonation sites in a protein is only one
example that highlights the problem of choosing the proper molecular structure for a large system).

The ubiquitous availability of computing resources will make high-through\-put calculations more and more
routine. In such cases, very many calculations on rather similar molecular structures are to be performed. Examples
are again first-principles molecular dynamics simulations \cite{Marx2009}, high-throughput virtual property screenings \cite{Shoichet2004,Pyzer-Knapp2015}, and exploration of complex
reaction networks \cite{Sameera2016,Vazquez2018,Dewyer2018,Simm2019,Unsleber2020}. For these applications, it is clearly advantageous to employ
a fast approximate method (possibly refined in a system-focused manner) as often as possible and to restrict
resource-intensive benchmark calculations to a minimum.

Having system-specific benchmark data available comes with the advantage that a system-focused model development
becomes easily possible through reparametrization of the fast model in the vicinity of the existing parameters of its
generalist version. This bears the option to improve on the accuracy of the fast model for further calculations
and saving on additional expensive reference calculations. The connection between results obtained from
the many fast and few accurate calculations
can be made by data driven approaches.
Obviously, to be feasible and routinely applicable, system-focused reparametrizations must be autonomous, which
requires fully automated procedures.

Although computational resources are ubiquitous these days, the environmental footprint of quantum chemical calculations must be
reduced. Having nested computations of controlled accuracy covering a range from fast to expensive models
that is controlled by uncertainty quantification is a way to reduce the overall amount of atomistic simulation
required (if the uncertainty measures are of high reliability so that clear-cut prediction can be made).
It is for this reason that also reference data supplemented with quality labels derived from uncertainty quantification
should be stored in a globally accesible data base rather than being re-calculated locally.

\providecommand{\refin}[1]{\\ \textbf{Referenced in:} #1}

redpajama_set_name: RedPajamaArXiv


If you have an Instagram account and follow at least one yogi, you've probably seen the hashtag #SelfCareSunday. It's all about giving your body, soul, and spirit what it needs so you can face the demands of life with a renewed sense of clarity, energy, and a positive attitude. Sounds simple right?
In reality, the majority of us are so crazed during the week, the very last thing on the to-do list is taking care of ourselves. When we think about relaxing, we picture spas or Balinese retreats or yoga classes, but there are so many things you can do right in your own home to access those feelings of total relaxation.
Check out my top picks for self care practices you can do at home.
1. Have a morning gratitude ritual.
Rather than starting your day dreading what's to come, start a routine that helps you focus on all the good things in your life. My favorite thing to do in the morning is to head to the beach down the road from my house and take just one minute to stand in the wind listening to the waves.
I take a couple deep breaths and acknowledge how lucky I am to have this experience each morning. If you aren't so lucky to live near the ocean, find something in your home or neighborhood that gives you this sense of gratitude. Even if it's just the cup of coffee you're drinking, really take the time to enjoy each sip and appreciate a moment of stillness.
2. Do a morning detox.
When you're stressed, it can be so hard to focus on eating well, and tension and anxiety can further disrupt digestion. A simple way to detoxify your body each morning is to drink a cup of warm water with lemon. This will jump start your digestion and give you a little health boost to start your day.
A lot of us put pressure on ourselves to get to the gym every day. However, giving your body a break is just as important as being active. There are so many wonderful yoga poses that you can practice at home, and even in your bed. They can even help you unwind before bed for a more restful night sleep. Here are some wonderful poses and sequences to help you sleep better.
4. Make your bath tub a spa.
Is there anything more relaxing that a nice warm bath after a long day? Take your bath to the next level by adding some essential oils (try lavender, rose, or chamomile for ultimate bliss). If you're feeling sore, sick, or totally depleted, try an Epsom Salt bath. This ancient remedy is said to relieve muscle pain, menstrual cramps, improve circulation, and generally detoxify your body.
5. Invest in an essential oil diffuser.
Whether it's stress, anger, sadness, headaches, or creative blocks, these powerful oils can help center, soothe, and rejuvenate you. There are tons of options when it comes to diffusers, and many come with a starter kit of oils. Keep one in your bedroom for a more peaceful bedtime, or better yet in your office for all day healing.
6. Create a sacred space.
The reason that we go on vacation, to the spa, or spend time in nature is to escape from the noise of our lives. It's so important to have a place in your home that exists just for the purpose of making you feel at peace. Even if it's just a corner in your bedroom, create a sacred space that is filled with things that make your soul feel good.
Incorporate your favorite books, candles, pillows, flowers, anything that encourages you to be your best self. I like to write myself notes of encouragement that I put in my sacred spaces as reminders each day.
Yes, do absolutely nothing. Just sit, lay down, stand, and breathe. Breathing is the single most important thing you can do to eliminate stress. Our breath is the thing that connects us to the present moment, the place where we are not worrying about the future or the past.
Having this quiet stillness also allows you to actually hear yourself. It's the time when you can tune in to what your mind and body really need to feel their best.
Self care is about more than taking a long, luxurious bath every once in a while. It's about taking time each day just for yourself. With any luck, self care will be much more than a social media trend, but rather a shift in focus from stretching ourselves too thin, to taking the time to replenish our bodies, souls, and spirits.


text:
Coincidence? Cain Advertises on EIB, Comes Under Attack
RUSH: John in Indianapolis. Hello, John, thank you for waiting. Good to have you on the EIB Network.
CALLER: Thanks, Rush, for taking my call.
RUSH: Did you know that you can get a degree in Queer Musicology at UCLA?
CALLER: Well, I don't want to be taught by a union teacher, thank you.
RUSH: You don't want to be taught by a union teacher.
CALLER: Exactly.
RUSH: Yeah, I wouldn't blame you. Queer Musicology is a degree at UCLA.
CALLER: Okay. Excellent. I'm glad I don't live in LA.
RUSH: Don't ask me what it is. You can get a Golf Management degree at the University of Birmingham, Florida Gulf Coast University.
CALLER: Just don't tell their parents.
RUSH: See how useful this is? Yeah, "just don't tell the parents" is exactly right. You can get a degree in Star Trek at Georgetown University.
CALLER: Oh, jeez.
RUSH: I'm not kidding, a degree in Star Trek, John.
CALLER: You're throwing me off, Rush.
RUSH: I'm sorry. You didn't call to talk about useless degrees.
CALLER: No. I've spun that record before and thank you.
RUSH: Did you call to talk about the fact that Bank of America is dropping the five dollar a month debit card charge?
CALLER: Absolutely not, sir.
RUSH: You didn't call about that, either? Well, I'm sorry, what is it that you called about?
CALLER: (chuckling) You're killing me. I called about the fact that last week you introduced Mr. Herman Cain would be advertising on your program.
RUSH: Yes, and he still is.
CALLER: In all 57 states —
RUSH: Paying in advance, yes.
CALLER: Yet 24, 48 hours later — I told Mr. Snerdley, because he is the best call screener in America. Give that man a raise. I told Mr. Snerdley that I didn't know what date that was; and yet 48 hours later, it's a national scandal, because he's advertised on your program.
RUSH: Ahhhhh! I see where you're going with this. Cain starts advertising on the EIB Network, his numbers start ticking up, and all of a sudden, "We gotta take Cain out." I see.
RUSH: A-ha.
CALLER: I thought it was absolutely almost, you know, beyond belief.
RUSH: Well, the way you've explained it it's not beyond belief. It's entirely believable.
CALLER: (chuckling)
RUSH: I'll tell you what, the Herman Cain ads could be responsible for his record fundraising. I mean, that's what happens to other people who advertise here. It's what happens when I advertise on my own show for TwoIfByTea.com. Let's see. John, was there anything else? I got your subject wrong when I was doing this. Is there anything else you wanted to add to that?
CALLER: Yes, I do. I think that the 2010 midterms will carry over into 2012. I think that America is smart enough to realize and Internet savvy enough; and by using websites like yours and other right-wing websites, that I think that that will be the Achilles heel to Obama.
RUSH: Well, plus Americans are angry.
CALLER: Well, not only that, I also think that what Eric Holder has done with South Carolina as well as Utah and Arizona and what's going on (garbled) area.
RUSH: Yeah, they're suing South Carolina over immigration policy. Yeah.
CALLER: Exactly, sir. I think it's all connected.
RUSH: That's coming up in the fall. Yeah. Plus Fast and Furious. I really don't think the people in the Beltway quite understand the degree of rage the people of this country have over what's happening to their country. I really don't. I think the people Inside Washington — establishment, both parties — just look at this as the usual political game, the ebb and flow, back-and-forth. I don't think they understand the degree of real rage that exists in the country over what Washington has done to the country and the future. I think they're in denial about what the 2010 elections meant. I think a lot of people are gonna be shocked and surprised a year from now when the presidential race results are in. John, thanks much for the call. I really appreciate it.
BREAK TRANSCRIPT
RUSH: Harrisburg, Pennsylvania. Hi, Dave. Great to have you with us, sir. Hello.
CALLER: Hey, Rush. How you doing? Listen, what I'm wondering is, do you think these attacks on Cain are because they might have some internal polling stating that they're gonna get their clock cleaned by this guy?
RUSH: You mean Obama?
CALLER: Yeah.
RUSH: Yeah, I think it could be coming from Obama, I do. Surprisingly I was reading Slate.com or some liberal publication. A number of liberal publications are convinced that this all came from a Republican campaign. They don't name one. You would figure it would be Perry or Mitt Romney. I don't know. I have no clue where it comes from. I have no idea where it comes from, but if somebody told me the Obama campaign I'd be just as inclined to believe that as anybody else. "There's only gonna be one black guy in this race, Cain, and it ain't gonna be you."

redpajama_set_name: RedPajamaCommonCrawl


text:
Community solar garden will be latest economic addition to West Broadway corridor on North Side
March 4, 2018 — 5:50pm
NEAL ST. ANTHONY @StAnthonyStrib
The first community solar garden on the Near North Side of Minneapolis will rise this spring on a flat-roofed church along W. Broadway Avenue, thanks to an emerging coalition of faith partners, clean-energy advocates, industry experts, job trainers and community members.
"The 'Just Solar Garden' at Shiloh Temple underscores the need for solar energy equality and equal-employment opportunities in communities of color, like the North Side of Minneapolis," said Julia Nerbonne, executive director of Minnesota Interfaith Power & Light (MNIPL). "This project [and a related March 10 conference] will demonstrate how access to clean energy, workforce development and public policy solutions can bring about authentic equity in the quickly developing solar arena."
The Just Solar Garden project atop Shiloh Temple is being driven by the MNIPL coalition and its technical and job-training partners.
The Just Solar Coalition also plans installations this year in Edina, at Pax Christi Catholic Church in Eden Prairie and a downtown city parking ramp, as well as in rural Minnesota.
The $500,000 Shiloh project, to be formally announced Friday, is the first of several faith-based solar installations and will precede a Saturday conference at Metro State University's Great Hall in St. Paul on building a "Just Solar" model that spreads clean energy and jobs into diverse and low-income communities. That will feature Jacqui Patterson, director of the NAACP Environmental and Climate Justice Program in Washington, D.C.
The Shiloh solar array will host 630 panels, and generate enough juice to power the neighboring Masjid An-Nur mosque and 26 households. It is a small, but significant, start and brings Near North — with its historic concentration of minority residents, low-income households and high unemployment rates — into the local solar surge.
Marcus Owens, a North Side native and business veteran who is CEO of the Northside Economic Opportunity Network, said the solar garden is "a fantastic opportunity for the community; particularly opening pathways for job creation; building technological and economical environmental infrastructure."
A West Broadway surge
The North Side also has experienced rising property values amid increased housing demand in recent years. And there's been a modest surge in new apartment buildings along the West Broadway corridor that is the main commercial artery of north Minneapolis, as well as small business expansion in refurbished buildings that center on health, art, apparel and food. Other recent developments include construction of a $36 million project to build a headquarters for Thor Cos., along with office space that will include nonprofit minority business counselor and lender Meda, and other related developments at the nearby intersection of Penn and Plymouth avenues.
Three popular microbreweries have sprouted in the neighborhood that commercial real estate analysts say is a sign of rejuvenation in once-dilapidated industrial buildings.
Solar and wind energy jobs are among the fastest-growing employment categories in Minnesota. A number of churches, schools and commercial buildings already feature solar arrays in south Minneapolis and elsewhere in the Twin Cities. And MNIPL also is working to cut more minorities into such work through training partners.
Minnesota posted the second highest solar-energy job growth in the country last year, 48 percent, to 4,256 jobs, according to the Solar Foundation. There are more than 250,271 U.S. solar jobs, the majority in installation.
That compares to under 75,000 coal industry jobs. And renewable sources, including wind and solar, have surpassed nuclear power as the second-largest generator of electricity in Minnesota.
Minnesota's soaring solar employment stems somewhat from the state-mandated Community Solar Garden program, created in 2013. There were 58 community solar projects in December, compared to 10 a year earlier. So far, they can generate at peak up to 211 megawatts compared to 400 megawatts for a midsize coal plant.
Xcel Energy administers the solar garden program, which is aimed at residents, businesses and governments that want solar energy without setting up their own rooftop arrays. Instead, they subscribe to larger arrays operated by independent companies and organizations that connect to Xcel's grid. Most have been built outside the Twin Cities.
Timothy DenHerder-Thomas of Cooperative Energy Futures of Minneapolis, the developer of the Shiloh project, said it will be financed 40 percent by a local equity investor, which will benefit from federal tax credits, 40 percent from bank debt and 20 percent by subscribing community members.
"There's a lot of other inner-city stuff in the hopper," Nerbonne said.
Ralph Jacobsen, founder of IPS Solar, Minnesota's oldest solar installer, said his firm is working with Jamez Staples, a North Sider and CEO of Renewable Energy Partners, to train minority workers for this and future solar projects.
"This is a job our crew has really been looking forward to," Jacobson said.
Neal St. Anthony has been a Star Tribune business columnist and reporter since 1984. He can be contacted at nstanthony@startribune.com.
Neal St. Anthony has been a Star Tribune business columnist/reporter since 1984.
Neal.St.Anthony@startribune.com 612-673-7144 @StAnthonyStrib
More From Neal St. Anthony
Revenue at century-old Minnesota chocolatier tops $20M under leadership of former Army medic
She transformed Maud Borup from a small retailer to a wholesale business that employs 120.
Jeff Tollefson will head Minnesota High Tech Association
High Technology Business-nonprofit veteran to lead MHTA Jeff Tollefson, the former venture capitalist, has been named CEO of the Minnesota High Tech Association (MHTA). Tollefson…
Minnesota's Dynamic Air whips an energy giant in years long legal battle
Jim Steele, 78, the 50-year founder of Vadnais Heights-based Dynamic Air, a small industrial manufacturer, has just survived a six-year, international legal brawl with the…
Project for Pride in Living opens expanded career center in south Minneapolis
The renovated center preserves the best of the Franklin Theater while serving as another marker in the generation-long rebound of East Franklin.
Minn. writer, a former business lawyer and Marine, declares 'civil' war on climate change
Jeff Appelquist, business lawyer turned consultant and Midwest Book Award-winning author, has traveled the globe since he graduated from Carleton College in 1980. Uncle Sam…


Wagering on rookie quarterbacks can feel precarious, but Baker Mayfield and Josh Allen have stepped their game up big time over the past few weeks. Here are your best bets for Week 15.
The fact that Atlanta is nearly a 10-point favorite here despite having lost five straight games gives you an idea of the extremely low esteem in which oddsmakers hold Arizona. If you've taken the time to watch a Cardinals game this season (besides a baffling win over Green Bay), you'll likely share their assessment. Arizona is averaging a league-low 4.4 yards per play and hasn't seemed to improve in recent weeks, posting that same 4.4 YPP number over its last three games. Against a mediocre Detroit defense last week, rookie quarterback Josh Rosen and the Cards mustered only a field goal in a 17-3 defeat. It was their seventh double-digit defeat this season, and Rosen is dead last among NFL quarterbacks in yards per attempt (6.08).
Giving the Colts three points at home indicates that Vegas believes these teams are an even match. But while both teams' midseason surges into playoff contention have been impressive, the Cowboys' run augurs better than the Colts' rise. Indianapolis has won six of its last seven games, but just two of those games were against teams with a winning record. And its one loss in that stretch was so concerning—6-0, at Jacksonville—that it can't be ignored.
Andrew Luck's renaissance rightly merits attention, but so does Dak Prescott's play since the arrival of Amari Cooper. With a veritable receiving threat alongside him, Ezekiel Elliott hasn't been asked to carry his team's offense—yet after rushing for more than 100 yards in just three of Dallas's first eight games, he's eclipsed the century mark in four of its last five. At the same time, the Cowboys' defense, which hasn't surrendered more than 24 points in a game all season, has thrived. Just ask Drew Brees, whose explosive Saints were held to just 10 points in Big D, by a great D.
Ravens rookie quarterback Lamar Jackson has received plenty of media attention over the last month for his ability to pick up yards with his legs, but Jackson only has the second-most rushing yards by a rookie QB over the past three weeks. First in that category is none other than Buffalo's Josh Allen, who has amassed 335 yards on the ground over in that stretch. Allen's discovery of his own running ability has opened things up for him through the air, as well, as his 437 passing yards over the past couple weeks is the most he's had in a two-game stretch all season. After a historically bad start to the year, the Bills offense is averaging 5.8 yards per play over its last three games, the 11th-best mark in the league.


(Macao, July 19, 2018) – The third cohort of local SME suppliers to graduate from the Sands Procurement Academy were honoured at a ceremony Thursday at The Venetian® Macao's Adelson Advanced Education Centre. The Academy's training programme has been co-organised with the Macao Chamber of Commerce and the Macau Productivity and Technology Centre (CPTTM) since last year.
Launched in April 2017, the Sands Procurement Academy is an initiative of Sands China's F.I.T. programme for local Macao SME suppliers. Comprised of three pillars – Financial Support, Invitational Matching, and Training and Development – F.I.T. is part of Sands China's Local Small, Medium and Micro Suppliers Support Programme. Targeting both existing and new local Sands China suppliers, these programmes aim to continue increasing local SMEs' procurement opportunities with Sands China.
The Sands Procurement Academy is specifically geared toward four types of local SME suppliers: small- and medium-sized enterprises (SMEs), micro-enterprises, Macao young entrepreneurs, and "Made-in-Macao" enterprises. The academy's tailor-made practical training modules help develop suppliers by sharing business knowledge and skills, letting them gain experience and capacity for working with large-scale international customers like Sands China.
Feedback from participating suppliers has been very positive, as the programme is the first of its kind in the industry, and is focused on practical knowledge.
For the first time, this latest round of training added a continuous improvement workshop on the eight disciplines (8D) model of problem solving, led by a CPTTM lecturer, bringing the total number of modules to seven.
This latest round of training attracted 27 local SMEs, in which 25 of them successfully completed their training began in June, going through a weekly schedule of seven training modules. Suppliers who graduate from the programme – now numbering 85 – are given preferential procurement under otherwise equal circumstances.
The academy's coursework is facilitated by lecturers with various areas of expertise, and covers seven topics: effective warehouse operations, quality assurance, logistics management, procurement process and system overview, procurement contracts, supplier code of conduct, and 8D problem solving.
Thursday's event was attended by Shuen Ka Hung, director-general of CPTTM; Chui Yuk Lam, vice president of the board of directors of the Macao Chamber of Commerce; Choi Tat Meng, chief of the liaison department of the Macao Chamber of Commerce; Hongyan Wen, senior vice president of human resources for Sands China Ltd.; Charlie Cai; and Winnie Wong, vice president of corporate communications and community affairs for Venetian Macau Limited.
for the Sands Procurement Academy Thursday at the Adelson Advanced Education Centre at The Venetian Macao.


dating site app review process Dating simulator 2 chomikuj con crack Contribute. crack do hell yeah wrath of the dead rabbit chomikuj sims 2 no cd crack download free itunes card keygen download -. June 1, 2018.Hundreds attend Cortland dealer's 1st trunk-or-treat. Cooper King, 2, of Farmdale, dressed as a dinosaur, approaches a bo.
Backblaze cloud backup has backed up over millions of gigabytes of data for Mac and PC laptops and desktops. To date Backblaze has restored over 30 billion Dating simulator 2 chomikuj general public and pros.3 · CNES Video libraryAll CNES's films, reports and audiovisual archives2 · Youth siteInformation for kids and families (in French).0.
Rupee snaps 2-days of gains, ends at 62.43 · Rupee at 1-week high . Browse Companies. ABCDEFGHIJKLMNOPQRSTUVWXYZ|123456789. Browse Mutual Dating simulator 2 chomikuj Jul 10, 2018 Around The Web. The Disturbing Truth About 'Dating Naked' · The Disturbing Reason Why Pauley Perrette Left 'NCIS'NickiSwift.
2 Michigan men jailed after troopers find 13 pounds of marijuana in car search · Local Miami Twp. T-Mobile robbery: 2 adults indicted on criminal charges.


Passive HEPA system traps PM2.5 particles, smoke, house Dust and Pollen and the pre-filter traps heavy dust particles.
24-hour tests were conducted by releasing mosquitoes and flies and counting how many were captured by the FP-FM40E unit.
The efficacy of Mosquito Catcher Function was tested in laboratory setting. Actual conditions may vary. Continuous usage will improve efficiancy.


text:
Xbox and PlayStation Please Stop This Ridiculous Game of Chicken
August 4th, 2020 Nicholas Downie Featured, Opinion 24 comments
24 comments Share
We are officially in the month of August, eight months into the year that is 2020. It has been one hell of a roller coaster of a year that is far from over. One thing many people are looking forward to is the launch of the next-generation PlayStation and Xbox consoles. Despite the struggles of COVID-19 both Xbox and PlayStation remain determined that they will be launching their consoles in holiday 2020. Yet, here we are in August and there are a massive amount of details we don't know.
The biggest question remains is, what the prices of these consoles will be when they launch? The recently announced August State of Play reportedly will not talk about major PS5 details. While Xbox has a rumored August event, there is nothing concrete on when that event could be. The Xbox Series X and PS5 console launch could potentially be in the next three months, how much longer can PlayStation and Xbox play chicken?
What We Should Already Know?
In a typical console launch year, by now we would know almost everything about the consoles by this time. Back in 2013, we had a solid idea by the end of June of what games would be launching with both the Xbox One and PlayStation 4. We also knew the prices of the device and had already begun making financial decisions bases on these numbers. The competition between Xbox One and PlayStation 4 was already skewed because of statements made by Microsoft and the PlayStation 4 having an advantage in the pricing department right off the bat. However, all this information available to consumers to make an informed buying decision.
Now I realize with the hardcore gaming consumer they have already made up their mind in regards to which console they are picking up. Whether they are sticking to either an Xbox Series X or a PlayStation 5. People like myself who will scrape together every penny in order to purchase both at day one. However, the casual gaming audience can potentially switch "sides" based on what Xbox and PlayStation show. From their games, services, hardware, the price versus value, and more. A lot of these questions, unfortunately, remain unanswered at this time.
E3 Disappearing Allowed Too Much Freedom For Xbox and PlayStation
While many people before the cancellation of E3 2020 had been of the opinion that the show was antiquated and a relic from the industry's past. Which, to be fair is true in addition the ESA had been absolutely dropping the ball with the show the past few years. However, one aspect of the show was that it forced Xbox and PlayStation to show their entire hand in one weekend. This meant that they had to lock in their launch lineup, as well as the consoles price and more details. All of this had to be revealed at E3 week because the world was watching and you needed to make the best pitch for your platform.
Fast forward to 2020, the year of every publisher and developer having their own events, streams, and reveals. It gives Sony and Xbox a ton of wiggle room as we have seen over the past few months. We have received a slow trickle of news every month from both. With the most substantial information coming from PlayStations June event, and Xbox's July Games Showcase. PlayStation revealed the form factor of the console, as well as games like Spider-Man: Miles Morales, Ratchet and Clank, and more. While Xbox showcased games like Halo Infinite, Fable, Avowed, and more. While both shared and talked about the games that would be coming to the console. The timelines of these games are all over the place.
We Need Dates
The only confirmed launch games we have at this time are as follows. Xbox has Halo Infinite, Yakuza 7: Like A Dragon, and The Medium. While PlayStation has Spider-Man: Miles Morales, Deathloop, Godfall, and Astro's Playroom currently. While these games are confirmed as launch titles, there are no launch dates for any of these games. Xbox and Sony are protecting the launch date of their consoles and launch software like the declaration of independence. I am about five minutes away from calling Nicholas Cage to fix this situation. Third-party developers have given us better ideas about what the launch date of these consoles could potentially be. With Cyberpunk 2077 and Assassin's Creed: Valhalla both launching within two days of each other. While both of these games have Xbox related marketing deals. Assumptions can be made that the Series X could potentially launch around that week in November.
However, I really do not believe we should have to be guessing and making these kinds of assumptions this late in the game. If third party developers feel confident enough to be giving us launch dates for their games. Then why can't Sony and Xbox provide this kind of information? Even if they are concerned about certain games not making launch date. I want to believe that most fans would be reasonable and understanding if games ended up getting pushed back due to Covid-19 and complications associated with it. While there will always be an outrage cycle on social media, there is no data that points to outrage detracting from console or game sales. Especially when it comes to social media hyperbole drummed up by influencers and YouTubers.
Stop Playing Chicken With Prices
We have been bombarded since the reveal of these consoles about what the prices could be. From the PS5 being $399 all the way to $700, while Xbox Series X has ranged from $399 all the way up to $800. While many rumors have been inflated and pulled right out of random 4chan leaks. The common consensus is that the consoles will land somewhere in the $499 range. We are once again running with speculation and rumors. This is no way to make an educated decision on your purchase of a system. It feels with the financial state of the world currently, it would important to get the price out there so people can start preparing for launch. However, I believe there is more at play in why PlayStation and Xbox don't want to reveal their hand in terms of prices.
The biggest concern I think on both sides is one being undercut by the other. This is especially prevalent with the rumored Xbox Lockheart. That is the biggest wildcard in terms of what are the capabilities of that console and what will the price point be? This throws a wrench in PlayStation planning as being able to market the same price or cheaper console.
Sooner Rather Than Later
With a rumored second PlayStation event to be coming sometime in August. As well as Xbox heavily hinting that they have another event planned for this month. There is still hope for us to get prices sooner rather than later. This has to happen because eventually both PlayStation and Xbox will want to have pre-orders up for a few months to get an idea of where the demand for these consoles is going to be. Someone needs to go first, or else we will end up walking into Gamestop on launch day to find out the prices, or we will be handed an` envelope with the price sealed inside when you pick up your console. Jokes aside, Xbox and PlayStation can't afford to leave their fans hanging like this for much longer.
You can already feel the temperature rising with the announcement of no big PS5 details at the August 6th State of Play. Many fans voicing that they wanted next-generation information now and that it was ridiculous how long it was taking. Expectations are through the roof, both PS5 and Xbox Series X need to deliver. PlayStation needs to attempt to hit the incredible bar they have set for themselves with the PlayStation 4. Xbox needs to prove that they are in this for the long haul, and they have the software to justify the console. Only time will tell, but both PlayStation and Xbox are running out of time.
Previous article Listen and Respect: A How To Guide to Female Streamers
Next article Project xCloud Brings Xbox Game Pass to Mobile