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.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
dataset 1
citation-polarity summary
fields
cond-mat.mtrl-sci 2verdicts
UNVERDICTED 2representative citing papers
MatterSim-MT is a multi-task ML foundation model pretrained on 35M+ structures for in silico materials property prediction and complex simulations.
citing papers explorer
-
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.