most citedA Hessian-Based Assessment of Atomic Forces for Training Machine Learning Interatomic Potentials

11 citations · 11 across the 1 of their papers we have counts for

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1 paper

cond-mat.mtrl-sci2021★ 11 cited

A Hessian-Based Assessment of Atomic Forces for Training Machine Learning Interatomic Potentials

Marius Herbold, Jörg Behler

In recent years, many types of machine learning potentials (MLPs) have been introduced, which are able to represent high-dimensional potential-energy surfaces (PES) with close to f…