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)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
verdicts
UNVERDICTED 2roles
dataset 1polarities
use dataset 1representative citing papers
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.
citing papers explorer
-
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.
-
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.