most citedRobust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling

3 citations · 5 across the 2 of their papers we have counts for

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2 papers

cond-mat.mtrl-sci2023★ 3 cited

Robust Training of Machine Learning Interatomic Potentials with Dimensionality Reduction and Stratified Sampling

Ji Qi, Tsz Wai Ko, Brandon C. Wood +2

Machine learning interatomic potentials (MLIPs) enable the accurate simulation of materials at larger sizes and time scales, and play increasingly important roles in the computatio…

physics.chem-ph2023★ 2 cited

Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding

Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker +1

In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemist…