Symmetry-adapted graph neural networks for constructing molecular dynamics force fields

8 January 2021

Papers citing "Symmetry-adapted graph neural networks for constructing molecular dynamics force fields"

5 / 5 papers shown

Title
Graph neural networks for materials science and chemistry Patrick Reiser Marlen Neubert André Eberhard Luca Torresi Chen Zhou ... Houssam Metni Clint van Hoesel Henrik Schopmans T. Sommer Pascal Friederich GNN AI4CE 59 377 0 05 Aug 2022
A simple equivariant machine learning method for dynamics based on scalars Weichi Yao Kate Storey-Fisher D. Hogg Soledad Villar AI4CE 44 9 0 07 Oct 2021
Heterogeneous relational message passing networks for molecular dynamics simulations Zun Wang Chong Wang Sibo Zhao Yong Xu Shaogang Hao Chang-Yu Hsieh B. Gu W. Duan 24 26 0 02 Sep 2021
Scalars are universal: Equivariant machine learning, structured like classical physics Soledad Villar D. Hogg Kate Storey-Fisher Weichi Yao Ben Blum-Smith PINN AI4CE 29 133 0 11 Jun 2021
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 295 1,956 0 09 Jun 2018