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DeePKS-kit: a package for developing machine learning-based chemically accurate energy and density functional models
Yixiao Chen, Linfeng Zhang, Han Wang +1
We introduce DeePKS-kit, an open-source software package for developing machine learning based energy and density functional models. DeePKS-kit is interfaced with PyTorch, an open-…
Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space
Wanrun Jiang, Yuzhi Zhang, Linfeng Zhang +1
Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface (PES) is the philosopher's stone for unraveling the natur…
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory
Yixiao Chen, Linfeng Zhang, Han Wang +1
We propose a general machine learning-based framework for building an accurate and widely-applicable energy functional within the framework of generalized Kohn-Sham density functio…
Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
Jianxing Huang, Linfeng Zhang, Han Wang +3
It has been a challenge to accurately simulate Li-ion diffusion processes in battery materials at room temperature using {\it ab initio} molecular dynamics (AIMD) due to its high c…
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…