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

Deep neural networks capable of representing the density functional theory (DFT)
Hamiltonian as a function of material structure hold great promise for revolutionizing future …

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

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 …

High-spin states of the odd-odd Pr 136 nucleus have been investigated using the Mo 100
(Ar 40, 1 p 3 n) reaction with the JUROGAM II γ-ray spectrometer. Many new transitions and …

This study presents an efficient methodology for simulating nonadiabatic dynamics of
complex materials with excitonic effects by integrating machine learning (ML) models with …

Deep learning for predicting the electronic-structure Hamiltonian of quantum systems
necessitates satisfying the covariance laws, among which achieving SO (3)-equivariance …

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 …

We propose a framework to combine strong non-linear expressiveness with strict SO (3)-
equivariance in prediction of the electronic-structure Hamiltonian, by exploring the …