Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
The ability to discover new materials with desirable properties is critical
for numerous applications from helping mitigate climate change to advances in
next generation computing hardware. AI has the potential to accelerate
materials discovery and design by more effectively exploring the chemical space
compared to other computational methods or by trial-and-error. While
substantial progress has been made on AI for materials data, benchmarks, and
models, a barrier that has emerged is the lack of publicly available training
data and open pre-trained models. To address this, we present a Meta FAIR
release of the Open Materials 2024 (OMat24) large-scale open dataset and an
accompanying set of pre-trained models. OMat24 contains over 110 million
density functional theory (DFT) calculations focused on structural and
compositional diversity. Our EquiformerV2 models achieve state-of-the-art
performance on the Matbench Discovery leaderboard and are capable of predicting
ground-state stability and formation energies to an F1 score above 0.9 and an
accuracy of 20 meV/atom, respectively. We explore the impact of model size,
auxiliary denoising objectives, and fine-tuning on performance across a range
of datasets including OMat24, MPtraj, and Alexandria. The open release of the
OMat24 dataset and models enables the research community to build upon our
efforts and drive further advancements in AI-assisted materials science.
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Luis Barroso-Luque
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Muhammed Shuaibi
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2024-10-18T08:13:58.305Z
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Muhammed Shuaibi
user
mshuaibi
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Xiang Fu
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Brandon M. Wood
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Misko Dzamba
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Meng Gao
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Ammar Rizvi
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C. Lawrence Zitnick
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2025-05-14T04:24:41.440Z
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Charles Zitnick
user
clz55
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Zachary W. Ulissi
extracted_pending
2024-10-18T12:19:06.410Z
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/avatars/05c98ccb37ce3c4ec83bfdae7c5b1b44.svg
Zachary Ulissi
user
zulissimeta
Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
The ability to discover new materials with desirable properties is critical
for numerous applications from helping mitigate climate change to advances in
next generation computing hardware. AI has the potential to accelerate
materials discovery and design by more effectively exploring the chemical space
compared to other computational methods or by trial-and-error. While
substantial progress has been made on AI for materials data, benchmarks, and
models, a barrier that has emerged is the lack of publicly available training
data and open pre-trained models. To address this, we present a Meta FAIR
release of the Open Materials 2024 (OMat24) large-scale open dataset and an
accompanying set of pre-trained models. OMat24 contains over 110 million
density functional theory (DFT) calculations focused on structural and
compositional diversity. Our EquiformerV2 models achieve state-of-the-art
performance on the Matbench Discovery leaderboard and are capable of predicting
ground-state stability and formation energies to an F1 score above 0.9 and an
accuracy of 20 meV/atom, respectively. We explore the impact of model size,
auxiliary denoising objectives, and fine-tuning on performance across a range
of datasets including OMat24, MPtraj, and Alexandria. The open release of the
OMat24 dataset and models enables the research community to build upon our
efforts and drive further advancements in AI-assisted materials science.
An open dataset and pre-trained models significantly advance AI-assisted materials discovery by providing extensive data and achieving state-of-the-art performance in predicting material properties.
Open Materials 2024 OMat24 density functional theory DFT EquiformerV2 Matbench Discovery ground-state stability formation energies auxiliary denoising objectives fine-tuning MPtraj Alexandria