Modern machine learning force fields (ML-FF) are able to yield energy and force predictions at the accuracy of high-level ab initio methods, but at a much lower computational cost. On the other hand, classical molecular mechanics force fields (MM-FF) employ fixed functional forms and tend to be less accurate, but considerably faster and transferable between molecules of the same class. In this work, we investigate how both approaches can complement each other. We contrast the ability of ML-FF for reconstructing dynamic and thermodynamic observables to MM-FFs in order to gain a qualitative understanding of the differences between the two approaches. This analysis enables us to modify the generalized AMBER force field by reparametrizing short-range and bonded interactions with more expressive terms to make them more accurate, without sacrificing the key properties that make MM-FFs so successful.
Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
Huziel E. Sauceda, Michael Gastegger, Stefan Chmiela, Klaus-Robert Müller, Alexandre Tkatchenko; Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields. J. Chem. Phys. 28 September 2020; 153 (12): 124109. https://doi.org/10.1063/5.0023005
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