2.3k citations · 6.8k across the 20 of their papers we have counts for
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physics.comp-ph2018★ 249 cited
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…
physics.chem-ph2018★ 706 cited
Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields
Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller +1
Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. How…