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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…
Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights
Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky +2
Highly accurate force fields are a mandatory requirement to generate predictive simulations. Here we present the path for the construction of machine learned molecular force fields…
Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces
Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky +2
We present the construction of molecular force fields for small molecules (less than 25 atoms) using the recently developed symmetrized gradient-domain machine learning (sGDML) app…