596 citations · 917 across the 14 of their papers we have counts for
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DeePKS Model for Halide Perovskites with the Accuracy of Hybrid Functional
Qi Ou, Ping Tuo, Wenfei Li +3
Accurate prediction for the electronic structure properties of halide perovskites plays a significant role in the design of highly efficient and stable solar cells. While density f…
DeePMD-kit v2: A software package for Deep Potential models
Jinzhe Zeng, Duo Zhang, Denghui Lu +44
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. T…
DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials
Wenfei Li, Qi Ou, Yixiao Chen +9
Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanica…
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-…
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…