CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling

28 February 2023

Papers citing "CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling"

2 / 2 papers shown

Title
SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects Oliver T. Unke Stefan Chmiela M. Gastegger Kristof T. Schütt H. E. Sauceda K. Müller 153 245 0 01 May 2021
E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials Simon L. Batzner Albert Musaelian Lixin Sun Mario Geiger J. Mailoa M. Kornbluth N. Molinari Tess E. Smidt Boris Kozinsky 198 1,232 0 08 Jan 2021