242 citations · 574 across the 14 of their papers we have counts for
14 papers
RLEKF: An Optimizer for Deep Potential with Ab Initio Accuracy
Siyu Hu, Wentao Zhang, Qiuchen Sha +5
It is imperative to accelerate the training of neural network force field such as Deep Potential, which usually requires thousands of images based on first-principles calculation a…
Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms
Zhuoqiang Guo, Denghui Lu, Yujin Yan +11
High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of \textit{ab initio} m…
DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models
Denghui Lu, Wanrun Jiang, Yixiao Chen +4
Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structur…
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,…
Fast real-time time-dependent density functional theory calculations with the parallel transport gauge
Weile Jia, Dong An, Lin-Wang Wang +1
Real-time time-dependent density functional theory (RT-TDDFT) is known to be hindered by the very small time step (attosecond or smaller) needed in the numerical simulation due to…
Robust Determination of the Chemical Potential in the Pole Expansion and Selected Inversion Method for Solving Kohn-Sham density functional theory
Weile Jia, Lin Lin
Fermi operator expansion (FOE) methods are powerful alternatives to diagonalization type methods for solving Kohn-Sham density functional theory (KSDFT). One example is the pole ex…
ELSI: A Unified Software Interface for Kohn-Sham Electronic Structure Solvers
Victor Wen-zhe Yu, Fabiano Corsetti, Alberto García +12
Solving the electronic structure from a generalized or standard eigenproblem is often the bottleneck in large scale calculations based on Kohn-Sham density-functional theory. This…
A Left-Looking Selected Inversion Algorithm and Task Parallelism on Shared Memory Systems
Mathias Jacquelin, Lin Lin, Weile Jia +2
Given a sparse matrix , the selected inversion algorithm is an efficient method for computing certain selected elements of . These selected elements correspond to all or…
SGO: An ultrafast engine for ab initio atomic structure global optimization by differential evolution
Zhanghui Chen, Weile Jia, Lin-Wang Wang
This paper presents a fast method for global search of atomic structures at ab initio level. The structures global optimization (SGO) engine consists of a high-efficiency different…