Optimal Invariant Bases for Atomistic Machine Learning

30 March 2025

Papers citing "Optimal Invariant Bases for Atomistic Machine Learning"

21 / 21 papers shown

Title
Cartesian atomic cluster expansion for machine learning interatomic potentials Bingqing Cheng 51 32 0 12 Feb 2024
An introduction to graphical tensor notation for mechanistic interpretability Jordan K. Taylor 47 2 0 02 Feb 2024
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products Shengjie Luo Tianlang Chen Aditi S. Krishnapriyan 47 21 0 18 Jan 2024
TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials Guillem Simeon Gianni De Fabritiis 46 45 0 10 Jun 2023
Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science D. P. Kovács Ilyes Batatia E. Arany Gábor Csányi AI4CE 51 88 0 23 May 2023
Transfer learning for chemically accurate interatomic neural network potentials Viktor Zaverkin David Holzmüller Luca Bonfirraro Johannes Kastner 73 24 0 07 Dec 2022
Tensor-reduced atomic density representations James P. Darby D. P. Kovács Ilyes Batatia M. A. Caro G. Hart Christoph Ortner Gábor Csányi 86 32 0 02 Oct 2022
MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields Ilyes Batatia D. P. Kovács G. Simm Christoph Ortner Gábor Csányi 59 457 0 15 Jun 2022
The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials Ilyes Batatia Simon L. Batzner D. P. Kovács Albert Musaelian G. Simm R. Drautz Christoph Ortner Boris Kozinsky Gábor Csányi 63 137 0 13 May 2022
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics Albert Musaelian Simon L. Batzner A. Johansson Lixin Sun Cameron J. Owen M. Kornbluth Boris Kozinsky 37 436 0 11 Apr 2022
TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials Philipp Thölke Gianni De Fabritiis AI4CE 45 191 0 05 Feb 2022
Equivariant message passing for the prediction of tensorial properties and molecular spectra Kristof T. Schütt Oliver T. Unke M. Gastegger 67 522 0 05 Feb 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 265 1,267 0 08 Jan 2021
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules Johannes Klicpera Shankari Giri Johannes T. Margraf Stephan Günnemann 52 319 0 28 Nov 2020
Machine Learning Force Fields Oliver T. Unke Stefan Chmiela H. E. Sauceda M. Gastegger I. Poltavsky Kristof T. Schütt A. Tkatchenko K. Müller AI4CE 78 897 0 14 Oct 2020
Directional Message Passing for Molecular Graphs Johannes Klicpera Janek Groß Stephan Günnemann 98 861 0 06 Mar 2020
Tune: A Research Platform for Distributed Model Selection and Training Richard Liaw Eric Liang Robert Nishihara Philipp Moritz Joseph E. Gonzalez Ion Stoica 128 887 0 13 Jul 2018
Group Normalization Yuxin Wu Kaiming He 139 3,626 0 22 Mar 2018
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds Nathaniel Thomas Tess E. Smidt S. Kearnes Lusann Yang Li Li Kai Kohlhoff Patrick F. Riley 3DPC 73 959 0 22 Feb 2018
Less is more: sampling chemical space with active learning Justin S. Smith B. Nebgen Nicholas Lubbers Olexandr Isayev A. Roitberg 47 604 0 28 Jan 2018
Hierarchical modeling of molecular energies using a deep neural network Nicholas Lubbers Justin S. Smith K. Barros AI4CE BDL 39 268 0 29 Sep 2017