OMat24 releases a new open dataset of 110M+ DFT calculations and EquiformerV2 models achieving SOTA on Matbench Discovery with F1>0.9 for stability and 20 meV/atom accuracy for formation energies.
Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations
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MatterSim delivers a single deep learning force field that simulates inorganic materials across elements, 0-5000 K, and up to 1000 GPa with near first-principles accuracy for lattice dynamics, mechanics, and Gibbs free energies.
The EDDP machine-learned potential for lead predicts the observed FCC-HCP phase transition at ~15 GPa, unlike EAM and MEAM models, when paired with nested sampling.
NEPMaker uses D-optimality active learning to identify and locally embed extrapolative atomic environments from large simulations into periodic structures for training neuroevolution potentials, aiming to cut extrapolation errors in complex materials.
Differentiable hybrid force fields combine physical models with neural corrections to enable fast, accurate, and calibratable simulations for scalable autonomous electrolyte discovery.
Deep learning architectures tailored to protein hierarchy combined with sequence-space search algorithms are used to improve prediction of protein complex structures and to design new interacting sequences.
citing papers explorer
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Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models
OMat24 releases a new open dataset of 110M+ DFT calculations and EquiformerV2 models achieving SOTA on Matbench Discovery with F1>0.9 for stability and 20 meV/atom accuracy for formation energies.
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MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
MatterSim delivers a single deep learning force field that simulates inorganic materials across elements, 0-5000 K, and up to 1000 GPa with near first-principles accuracy for lattice dynamics, mechanics, and Gibbs free energies.
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Benchmarking empirical and machine-learned interatomic potentials using phase diagram predictions for Lead
The EDDP machine-learned potential for lead predicts the observed FCC-HCP phase transition at ~15 GPa, unlike EAM and MEAM models, when paired with nested sampling.
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NEPMaker: Active learning of neuroevolution machine learning potential for large cells
NEPMaker uses D-optimality active learning to identify and locally embed extrapolative atomic environments from large simulations into periodic structures for training neuroevolution potentials, aiming to cut extrapolation errors in complex materials.
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Differentiable hybrid force fields support scalable autonomous electrolyte discovery
Differentiable hybrid force fields combine physical models with neural corrections to enable fast, accurate, and calibratable simulations for scalable autonomous electrolyte discovery.
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Deep Learning for Protein Complex Prediction and Design
Deep learning architectures tailored to protein hierarchy combined with sequence-space search algorithms are used to improve prediction of protein complex structures and to design new interacting sequences.