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20182021
most citedEnsemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach

61 citations · 61 across the 2 of their papers we have counts for

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physics.comp-ph2020★ 61 cited

Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach

Jiang Wang, Stefan Chmiela, Klaus-Robert Müller +2

Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a molecular potential and associated force field based on the kernel ridge regression algorit…

physics.comp-ph2018

sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning

Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky +2

We present an optimized implementation of the recently proposed symmetric gradient domain machine learning (sGDML) model. The sGDML model is able to faithfully reproduce global pot…