This review highlights recent advances in machine learning (ML)‐assisted design of energy
materials. Initially, ML algorithms were successfully applied to screen materials databases …

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 …

The discovery of advanced materials is the cornerstone of human technological
development and progress. The structures of materials and their corresponding properties …

Deep-learning density functional theory (DFT) shows great promise to significantly
accelerate material discovery and potentially revolutionize materials research. However …

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 …

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 …

In this study, we introduce a unified neural network architecture, the Deep Equilibrium
Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep …

Over the past decades, density functional theory (DFT) calculations have been utilized in
various fields such as materials science and semiconductor devices. However, due to the …

Deep learning electronic structures from ab initio calculations holds great potential to
revolutionize computational materials studies. While existing methods proved success in …

ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software
for first-principles electronic structure calculations and molecular dynamics simulations. It …