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20182020
most citedMolecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces

113 citations · 113 across the 1 of their papers we have counts for

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physics.comp-ph2019

Accurate Molecular Dynamics Enabled by Efficient Physically-Constrained Machine Learning Approaches

Stefan Chmiela, Huziel E. Sauceda, Alexandre Tkatchenko +1

We develop a combined machine learning (ML) and quantum mechanics approach that enables data-efficient reconstruction of flexible molecular force fields from high-level ab initio c…

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…