FeNNol: an Efficient and Flexible Library for Building
Force-field-enhanced Neural Network Potentials

FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials

2 May 2024

Thomas Plé

Louis Lagardère

Jean‐Philip Piquemal

Papers citing "FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials"

7 / 7 papers shown

Title
chemtrain: Learning Deep Potential Models via Automatic Differentiation and Statistical Physics Paul Fuchs Stephan Thaler Sebastien Röcken J. Zavadlav DiffM 61 5 0 28 Aug 2024
$${\it Asparagus}$: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces$ ${\it Asparagus}$ : A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces K. Töpfer Luis Itza Vazquez-Salazar Markus Meuwly 27 3 0 21 Jul 2024
Lifelong Machine Learning Potentials Marco Eckhoff Markus Reiher 54 20 0 10 Mar 2023
SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials Peter K. Eastman P. Behara David L. Dotson Raimondas Galvelis John E. Herr ... J. Chodera Benjamin P. Pritchard Yuanqing Wang Gianni de Fabritiis T. Markland 24 105 0 21 Sep 2022
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials Viktor Zaverkin Johannes Kastner 27 67 0 15 Sep 2021
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 142 242 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 188 1,218 0 08 Jan 2021