470 citations · 644 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…