27 citations · 52 across the 10 of their papers we have counts for
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physics.comp-ph2022★ 9 cited
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials
Cas van der Oord, Matthias Sachs, Dávid Péter Kovács +2
Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic…
math.NA2022
A framework for a generalisation analysis of machine-learned interatomic potentials
Christoph Ortner, Yangshuai Wang
Machine-learned interatomic potentials (MLIPs) and force fields (i.e. interaction laws for atoms and molecules) are typically trained on limited data-sets that cover only a very sm…
math.NA2022★ 1 cited
Optimal Evaluation of Symmetry-Adapted -Correlations Via Recursive Contraction of Sparse Symmetric Tensors
Illia Kaliuzhnyi, Christoph Ortner
We present a comprehensive analysis of an algorithm for evaluating high-dimensional polynomials that are invariant under permutations and rotations. The key bottleneck is the contr…