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5 papers
Machine Learning for Electron-phonon Interactions From Finite Difference
Zun Wang, Wenhui Duan, Zuzhang Lin
First-principles investigations of electron-phonon interactions (EPIs) play a crucial role in understanding a wide range of phenomena in physics and materials science. Among variou…
Heterogeneous relational message passing networks for molecular dynamics simulations
Zun Wang, Chong Wang, Sibo Zhao +5
With many frameworks based on message passing neural networks proposed to predict molecular and bulk properties, machine learning methods have tremendously shifted the paradigms of…
Deep-Learning Density Functional Theory Hamiltonian for Efficient ab initio Electronic-Structure Calculation
He Li, Zun Wang, Nianlong Zou +5
The marriage of density functional theory (DFT) and deep learning methods has the potential to revolutionize modern computational materials science. Here we develop a deep neural n…
Symmetry-adapted graph neural networks for constructing molecular dynamics force fields
Zun Wang, Chong Wang, Sibo Zhao +4
Molecular dynamics is a powerful simulation tool to explore material properties. Most of the realistic material systems are too large to be simulated with first-principles molecula…
Intrinsic magnetic topological insulators in van der Waals layered MnBiTe-family materials
Jiaheng Li, Yang Li, Shiqiao Du +6
The interplay of magnetism and topology is a key research subject in condensed matter physics and material science, which offers great opportunities to explore emerging new physics…