146 citations · 346 across the 4 of their papers we have counts for
8 papers
Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
Weile Jia, Han Wang, Mohan Chen +5
For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles. However, most AIMD application…
Ground state energy functional with Hartree-Fock efficiency and chemical accuracy
Yixiao Chen, Linfeng Zhang, Han Wang +1
We introduce the Deep Post-Hartree-Fock (DeePHF) method, a machine learning based scheme for constructing accurate and transferable models for the ground-state energy of electronic…
Raman Spectrum and Polarizability of Liquid Water from Deep Neural Networks
Grace M. Sommers, Marcos F. Calegari Andrade, Linfeng Zhang +2
We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application…
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
Yuzhi Zhang, Haidi Wang, Weijie Chen +4
In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simu…
Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics
Yuzhi Zhang, Chang Gao, Linfeng Zhang +2
Simulating warm dense matter that undergoes a wide range of temperatures and densities is challenging. Predictive theoretical models, such as quantum-mechanics-based first-principl…
Deep neural network for the dielectric response of insulators
Linfeng Zhang, Mohan Chen, Xifan Wu +3
We introduce a deep neural network to model in a symmetry preserving way the environmental dependence of the centers of the electronic charge. The model learns from ab-initio densi…