GSS unifies diffusion generation and random structure search into a single sampling process using learned scores and physical forces, recovering diverse metastable structures at over tenfold lower cost than pure RSS while working outside training distributions.
arXiv preprint arXiv:2210.00579 (2022)
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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.
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Generative structure search for efficient and diverse discovery of molecular and crystal structures
GSS unifies diffusion generation and random structure search into a single sampling process using learned scores and physical forces, recovering diverse metastable structures at over tenfold lower cost than pure RSS while working outside training distributions.
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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.