Computational Chemistry, Short talk
CC-013

Self-Parametrizing System-Focused Atomistic Models

C. Brunken1,2, M. Reiher1*
1ETH Zürich, Laboratory for Physical Chemistry, Vladimir-Prelog-Weg 2, 8093 Zurich, 2

Computational studies of chemical reactions in complex environments such as proteins or metal-organic frameworks require accurate and at the same time efficient atomistic models applicable to the nanometer scale. For arbitrary system classes, an accurate parametrization of the atomistic entities will not be available, but demands a fast automated system-focused parametrization procedure to be quickly applicable, reliable, flexible, and reproducible. We develop and combine [1] an automatically parametrizable quantum chemically derived molecular mechanics model with machine-learned corrections under uncertainty quantification. Our approach first generates an accurate, physically motivated model from a minimum energy structure and its corresponding Hessian matrix by a partial Hessian fitting procedure [2] of the force constants. This model can be applied to generate a large number of configurations (e.g., via molecular dynamics) for which additional reference data can be calculated on the fly. A Δ-machine learning model is trained on these data to provide a correction to energies and forces including uncertainty estimates. The parametrization of large systems is enabled by an autonomous fragmentation approach, which is demonstrated at the example of the copper-containing protein plastocyanin. Our approach may also be employed for the generation of system-focused electrostatic molecular mechanics embedding environments in a quantum-mechanical/molecular-mechanical hybrid model for arbitrary atomistic structures at the nanoscale.

[1] Brunken, C.; Reiher, M., J. Chem. Theory Comput. 2020, 16, 1646-1665.
[2] Wang, R.; Ozhgibesov, M.; Hirao, H., J. Comput. Chem. 2016, 37, 2349-2359.