Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Abstract

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.

19 pages

Cited by in corpus (12)

• On the design space between molecular mechanics and machine learning force fields
• Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals
• Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy
• A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons
• Materials discovery acceleration by using condition generative methodology
• Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost
• Fine-Tuning Unifies Foundational Machine-learned Interatomic Potential Architectures at ab initio Accuracy
• Discovery of sustainable energy materials via the machine-learned material space
• MC3D: The Materials Cloud computational database of experimentally known stoichiometric inorganics
• Machine Learning Phonon Spectra for Fast and Accurate Optical Lineshapes of Defects
• Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS2
• Tailored Vapor Deposition Unlocks Large-Grain, Wafer-Scale Epitaxial Growth of 2D Magnetic CrCl3