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Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields

Journal of Chemical Physics, vol. 153 (2020), pp. 124109


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.

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