Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
Abstract
The goal of the present work is to perform a detailed investigation of the differences between both systems based
on a set of small molecules exhibiting different quantum
mechanical phenomena. Based on these results, different
alternatives are explored for improving the data generation process and their applicability context for expediting the force-field learning procedure. Furthermore,
improvement of the accuracy for MM-FFs is studied by
reparameterising them based on more accurate reference
data and test their limits and functional form flexibility. For this task, we use the recently published sGDML
framework[25, 26] as ML-FF of choice, as it is able to efficiently reconstruct the potential energy surfaces (PES)
of medium sized molecules. The investigated systems are
the molecules ethanol, the keto and enol forms of malondialdehyde (keto-MDA and enol-MDA, respectively) as
well as salicylic and acetylsalicylic acid (Aspirin). In the
context of these systems, we study the performance of
MM-FFs and sGDML derived FFs based on the overall
reliability of the generated PESs, as well as effects arising from chemical phenomena such as hydrogen transfer
and orbital interactions. Although we restrict ourselves
to the sGDML approach, it can nevertheless be expected
that the results found here are equally valid for ML-FFs
in general.
Research Areas
