Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials

9 October 2022

Papers citing "Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials"

6 / 6 papers shown

Title
A practical guide to machine learning interatomic potentials -- Status and future Ryan Jacobs D. Morgan Siamak Attarian Jun Meng Chen Shen ... K. J. Schmidt So Takamoto Aidan Thompson Julia Westermayr Brandon M. Wood 55 4 0 12 Mar 2025
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials Matthias Holzenkamp Dongyu Lyu Ulrich Kleinekathöfer Peter Zaspel 33 0 0 10 Jan 2025
Physics-Informed Weakly Supervised Learning for Interatomic Potentials Makoto Takamoto Viktor Zaverkin Mathias Niepert AI4CE 55 0 0 23 Jul 2024
Consensus-based construction of high-dimensional free energy surface Liyao Lyu Huan Lei 9 0 0 08 Nov 2023
Nested sampling for physical scientists G. Ashton N. Bernstein Johannes Buchner Xi Chen Gábor Csányi ... Leah F. South J. Veitch Philipp Wacker D. Wales David Yallup 31 76 0 31 May 2022
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 1,229 0 08 Jan 2021