Equivariant neural network force fields for magnetic materials
Neural network force fields have significantly advanced ab initio atomistic simulations across
diverse fields. However, their application in the realm of magnetic materials is still in its early …
diverse fields. However, their application in the realm of magnetic materials is still in its early …
Improving density matrix electronic structure method by deep learning
The combination of deep learning and ab initio materials calculations is emerging as a
trending frontier of materials science research, with deep-learning density functional theory …
trending frontier of materials science research, with deep-learning density functional theory …
Neural-network density functional theory
Deep-learning density functional theory (DFT) shows great promise to significantly
accelerate material discovery and potentially revolutionize materials research, which …
accelerate material discovery and potentially revolutionize materials research, which …
[HTML][HTML] Universal materials model of deep-learning density functional theory Hamiltonian
Realizing large materials models has emerged as a critical endeavor for materials research
in the new era of artificial intelligence, but how to achieve this fantastic and challenging …
in the new era of artificial intelligence, but how to achieve this fantastic and challenging …
Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models
In this study, we introduce a unified neural network architecture, the Deep Equilibrium
Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep …
Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep …
Deep learning density functional theory Hamiltonian in real space
Deep learning electronic structures from ab initio calculations holds great potential to
revolutionize computational materials studies. While existing methods proved success in …
revolutionize computational materials studies. While existing methods proved success in …
A Framework of SO (3)-equivariant Non-linear Representation Learning and its Application to Electronic-Structure Hamiltonian Prediction
S Yin, X Pan, F Wang, F Wu, L He - arXiv preprint arXiv:2405.05722, 2024 - arxiv.org
We present both a theoretical and a methodological framework that addresses a critical
challenge in applying deep learning to physical systems: the reconciliation of non-linear …
challenge in applying deep learning to physical systems: the reconciliation of non-linear …