Fast and Sample-Efficient Interatomic Neural Network Potentials for
Molecules and Materials Based on Gaussian Moments

Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments

20 September 2021

Viktor Zaverkin

David Holzmüller

Johannes Kastner

Papers citing "Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments"

9 / 9 papers shown

Title
$Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $\mathrm{CO_2}$ hydrogenation energy barriers$ Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $\mathrm{CO_2}$ hydrogenation energy barriers Luuk H. E. Kempen Marius Juul Nielsen Mie Andersen 23 0 0 23 Apr 2025
Physics-Informed Weakly Supervised Learning for Interatomic Potentials Makoto Takamoto Viktor Zaverkin Mathias Niepert AI4CE 60 0 0 23 Jul 2024
Higher-Rank Irreducible Cartesian Tensors for Equivariant Message Passing Viktor Zaverkin Francesco Alesiani Takashi Maruyama Federico Errica Henrik Christiansen Makoto Takamoto Nicolas Weber Mathias Niepert 46 5 0 23 May 2024
Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials Viktor Zaverkin David Holzmüller Henrik Christiansen Federico Errica Francesco Alesiani Makoto Takamoto Mathias Niepert Johannes Kastner AI4CE 29 13 0 03 Dec 2023
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Moments Viktor Zaverkin Julia Netz Fabian Zills Andreas Köhn Johannes Kastner AI4CE 22 16 0 03 Dec 2023
Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water Viktor Zaverkin David Holzmüller Robin Schuldt Johannes Kastner 25 15 0 03 Dec 2023
Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science D. P. Kovács Ilyes Batatia E. Arany Gábor Csányi AI4CE 24 82 0 23 May 2023
Transfer learning for chemically accurate interatomic neural network potentials Viktor Zaverkin David Holzmüller Luca Bonfirraro Johannes Kastner 23 24 0 07 Dec 2022
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials Viktor Zaverkin Johannes Kastner 34 67 0 15 Sep 2021