DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

11 December 2017

Papers citing "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics"

21 / 21 papers shown

Title
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction Robert Appleton Brian C Barnes Alejandro Strachan FedML AI4CE 34 0 0 09 Apr 2025
A practical guide to machine learning interatomic potentials -- Status and future Ryan Jacobs D. Morgan Siamak Attarian Jun Meng Chen Shen ... K. J. Schmidt So Takamoto Aidan Thompson Julia Westermayr Brandon M. Wood 59 4 0 12 Mar 2025
Learning local equivariant representations for quantum operators Zhanghao Zhouyin Zixi Gan MingKang Liu S. K. Pandey Linfeng Zhang Qiangqiang Gu 85 3 0 28 Jan 2025
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs Yuanchang Zhou Siyu Hu Chen Wang Lin-Wang Wang Guangming Tan Weile Jia AI4CE GNN 52 0 0 30 Dec 2024
Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential Kentaro Hino Yuki Kurashige 34 0 0 31 Oct 2024
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics Paul Fuchs Stephan Thaler Sebastien Röcken Julija Zavadlav DiffM 72 6 0 28 Aug 2024
PWDFT-SW: Extending the Limit of Plane-Wave DFT Calculations to 16K Atoms on the New Sunway Supercomputer Qingcai Jiang Zhenwei Cao Junshi Chen Xinming Qin Wei Hu Hong An Jinlong Yang 15 2 0 16 Jun 2024
Machine-Learned Atomic Cluster Expansion Potentials for Fast and Quantum-Accurate Thermal Simulations of Wurtzite AlN Guang Yang Yuan Liu Lei Yang Bingyang Cao AI4CE 34 6 0 20 Nov 2023
A Heterogeneous Parallel Non-von Neumann Architecture System for Accurate and Efficient Machine Learning Molecular Dynamics Zhuoying Zhao Ziling Tan Pinghui Mo Xiaonan Wang Dan Zhao Xin Zhang Ming Tao Jie Liu 21 1 0 26 Mar 2023
Neural DAEs: Constrained neural networks Tue Boesen E. Haber Uri M. Ascher 33 3 0 25 Nov 2022
Utilising physics-guided deep learning to overcome data scarcity Jinshuai Bai Laith Alzubaidi Qingxia Wang E. Kuhl Bennamoun Yuantong T. Gu PINN AI4CE 31 3 0 24 Nov 2022
Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism Zimu Li Zihan Pengmei Han Zheng Erik H. Thiede Junyu Liu Risi Kondor 29 2 0 14 Nov 2022
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials Cas van der Oord Matthias Sachs D. P. Kovács Christoph Ortner Gábor Csányi 41 64 0 09 Oct 2022
AI-coupled HPC Workflows S. Jha V. Pascuzzi Matteo Turilli 24 9 0 24 Aug 2022
Edge-based Tensor prediction via graph neural networks Yang Zhong Hongyu Yu X. Gong H. Xiang 13 2 0 15 Jan 2022
Graph Neural Networks Accelerated Molecular Dynamics Zijie Li Kazem Meidani Prakarsh Yadav A. Farimani GNN AI4CE 21 53 0 06 Dec 2021
Complex Spin Hamiltonian Represented by Artificial Neural Network Hongyu Yu Changsong Xu Feng Lou L. Bellaiche Zhenpeng Hu X. Gong H. Xiang 26 15 0 02 Oct 2021
Symmetry-adapted graph neural networks for constructing molecular dynamics force fields Zun Wang Chong Wang Sibo Zhao Shiqiao Du Yong Xu B. Gu W. Duan AI4CE 27 14 0 08 Jan 2021
Learning Thermodynamically Stable and Galilean Invariant Partial Differential Equations for Non-equilibrium Flows Juntao Huang Zhiting Ma Y. Zhou W. Yong AI4CE 35 16 0 28 Sep 2020
Machine learning for electronically excited states of molecules Julia Westermayr P. Marquetand 17 257 0 10 Jul 2020
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation Linfeng Zhang De-Ye Lin Han Wang R. Car E. Weinan 9 326 0 28 Oct 2018