36 citations · 55 across the 4 of their papers we have counts for
9 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…
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
Denghui Lu, Han Wang, Mohan Chen +6
We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation…
ELSI -- An Open Infrastructure for Electronic Structure Solvers
Victor Wen-zhe Yu, Carmen Campos, William Dawson +16
Routine applications of electronic structure theory to molecules and periodic systems need to compute the electron density from given Hamiltonian and, in case of non-orthogonal bas…
Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks
Leonardo Zepeda-Núñez, Yixiao Chen, Jiefu Zhang +3
The recently developed Deep Potential [Phys. Rev. Lett. 120, 143001, 2018] is a powerful method to represent general inter-atomic potentials using deep neural networks. The success…
Parallel Transport Time-Dependent Density Functional Theory Calculations with Hybrid Functional on Summit
Weile Jia, Lin-Wang Wang, Lin Lin
Real-time time-dependent density functional theory (rt-TDDFT) with hybrid exchange-correlation functional has wide-ranging applications in chemistry and material science simulation…
Fast real-time time-dependent hybrid functional calculations with the parallel transport gauge and the adaptively compressed exchange formulation
Weile Jia, Lin Lin
We present a new method to accelerate real time-time dependent density functional theory (rt-TDDFT) calculations with hybrid exchange-correlation functionals. For large basis set,…