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We present the construction of molecular force fields for small molecules(less than 25 atoms) using the recently developed symmetrized gradient-domainmachine learning (sGDML) approach [Chmiela et al., Nat. Commun. 9, 3887 (2018);Sci. Adv. 3, e1603015 (2017)]. This approach is able to accurately reconstructcomplex high-dimensional potential-energy surfaces from just a few 100s ofmolecular conformations extracted from ab initio molecular dynamicstrajectories. The data efficiency of the sGDML approach implies that atomicforces for these conformations can be computed with high-levelwavefunction-based approaches, such as the "gold standard" CCSD(T) method. Wedemonstrate that the flexible nature of the sGDML model recovers local andnon-local electronic interactions (e.g. H-bonding, proton transfer, lone pairs,changes in hybridization states, steric repulsion and $n\to\pi^*$ interactions)without imposing any restriction on the nature of interatomic potentials. Theanalysis of sGDML molecular dynamics trajectories yields new qualitativeinsights into dynamics and spectroscopy of small molecules close tospectroscopic accuracy.

journal of chemical physics online/the journal of chemical physics/journal of chemical physics

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