1.6k citations · 4k across the 16 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★ 382 cited
i-PI 2.0: A Universal Force Engine for Advanced Molecular Simulations
Venkat Kapil, Mariana Rossi, Ondrej Marsalek +23
Progress in the atomic-scale modelling of matter over the past decade has been tremendous. This progress has been brought about by improvements in methods for evaluating interatomi…