3 papers
cs.LG2024
Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
Zun Wang, Chang Liu, Nianlong Zou +5
In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Mode…
cs.LG2024
Self-Consistency Training for Density-Functional-Theory Hamiltonian Prediction
He Zhang, Chang Liu, Zun Wang +5
Predicting the mean-field Hamiltonian matrix in density functional theory is a fundamental formulation to leverage machine learning for solving molecular science problems. Yet, its…
cond-mat.mtrl-sci2024
MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
Han Yang, Chenxi Hu, Yichi Zhou +19
Accurate and fast prediction of materials properties is central to the digital transformation of materials design. However, the vast design space and diverse operating conditions p…