title: Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
summary: 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.
paper_authors._id: 67112541c8d0ef79daa54ed8
paper_authors.name: Luis Barroso-Luque
paper_authors._id: 67112541c8d0ef79daa54ed9
paper_authors.name: Muhammed Shuaibi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-18T08:13:58.305Z
paper_authors._id: 67004f02d66ad0efb0d494c3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/67004f02d66ad0efb0d494c3/eIGYDUVAovab9ssRFotAK.jpeg
paper_authors.fullname: Muhammed Shuaibi
paper_authors.type: user
paper_authors.user: mshuaibi
paper_authors._id: 67112541c8d0ef79daa54eda
paper_authors.name: Xiang Fu
paper_authors._id: 67112541c8d0ef79daa54edb
paper_authors.name: Brandon M. Wood
paper_authors._id: 67112541c8d0ef79daa54edc
paper_authors.name: Misko Dzamba
paper_authors._id: 67112541c8d0ef79daa54edd
paper_authors.name: Meng Gao
paper_authors._id: 67112541c8d0ef79daa54ede
paper_authors.name: Ammar Rizvi
paper_authors._id: 67112541c8d0ef79daa54edf
paper_authors.name: C. Lawrence Zitnick
paper_authors._id: 67112541c8d0ef79daa54ee0
paper_authors.name: Zachary W. Ulissi
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2024-10-18T12:19:06.410Z
paper_authors._id: 6712520b971ea2188b33f7d4
paper_authors.avatarUrl: /avatars/05c98ccb37ce3c4ec83bfdae7c5b1b44.svg
paper_authors.fullname: Zachary Ulissi
paper_authors.type: user
paper_authors.user: zulissimeta
paper_title: Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
paper_summary: 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.
paper_ai_keywords: density functional theory (DFT) EquiformerV2 Matbench Discovery F1 score ground-state stability formation energies model size auxiliary denoising objectives fine-tuning