Training a Foundation Model for Materials on a Budget
Foundation models for materials modeling are advancing quickly, but their
training remains expensive, often placing state-of-the-art methods out of reach
for many research groups. We introduce Nequix, a compact E(3)-equivariant
potential that pairs a simplified NequIP design with modern training practices,
including equivariant root-mean-square layer normalization and the Muon
optimizer, to retain accuracy while substantially reducing compute
requirements. Built in JAX, Nequix has 700K parameters and was trained in 500
A100-GPU hours. On the Matbench-Discovery and MDR Phonon benchmarks, Nequix
ranks third overall while requiring less than one quarter of the training cost
of most other methods, and it delivers an order-of-magnitude faster inference
speed than the current top-ranked model. We release model weights and fully
reproducible codebase at https://github.com/atomicarchitects/nequix
68ac77f686b21a0e2e358c4d
Teddy Koker
claimed_verified
2025-08-25T19:31:49.935Z
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Teddy Koker
user
teddykoker
68ac77f686b21a0e2e358c4e
Tess Smidt
Training a Foundation Model for Materials on a Budget
Foundation models for materials modeling are advancing quickly, but their
training remains expensive, often placing state-of-the-art methods out of reach
for many research groups. We introduce Nequix, a compact E(3)-equivariant
potential that pairs a simplified NequIP design with modern training practices,
including equivariant root-mean-square layer normalization and the Muon
optimizer, to retain accuracy while substantially reducing compute
requirements. Built in JAX, Nequix has 700K parameters and was trained in 500
A100-GPU hours. On the Matbench-Discovery and MDR Phonon benchmarks, Nequix
ranks third overall while requiring less than one quarter of the training cost
of most other methods, and it delivers an order-of-magnitude faster inference
speed than the current top-ranked model. We release model weights and fully
reproducible codebase at https://github.com/atomicarchitects/nequix
Nequix, a compact E(3)-equivariant potential, achieves high accuracy with reduced computational cost and faster inference speed compared to other methods on materials modeling benchmarks.
E(3)-equivariant NequIP equivariant root-mean-square layer normalization Muon optimizer JAX Matbench-Discovery MDR Phonon benchmarks