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

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

11 December 2017
Han Wang
Linfeng Zhang
Jiequn Han
E. Weinan
    AI4CE
ArXivPDFHTML

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
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction
Robert Appleton
Brian C Barnes
Alejandro Strachan
FedML
AI4CE
37
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
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
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
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
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics
Paul Fuchs
Stephan Thaler
Sebastien Röcken
Julija Zavadlav
DiffM
75
6
0
28 Aug 2024
PWDFT-SW: Extending the Limit of Plane-Wave DFT Calculations to 16K
  Atoms on the New Sunway Supercomputer
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
17
2
0
16 Jun 2024
Machine-Learned Atomic Cluster Expansion Potentials for Fast and
  Quantum-Accurate Thermal Simulations of Wurtzite AlN
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
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
Neural DAEs: Constrained neural networks
Tue Boesen
E. Haber
Uri M. Ascher
36
3
0
25 Nov 2022
Utilising physics-guided deep learning to overcome data scarcity
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
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
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
AI-coupled HPC Workflows
S. Jha
V. Pascuzzi
Matteo Turilli
24
9
0
24 Aug 2022
Edge-based Tensor prediction via graph neural networks
Edge-based Tensor prediction via graph neural networks
Yang Zhong
Hongyu Yu
X. Gong
H. Xiang
15
2
0
15 Jan 2022
Graph Neural Networks Accelerated Molecular Dynamics
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
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
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
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
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
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
1