Journal
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Volume 16, Issue 3, Pages 1646-1665Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.9b00855
Keywords
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Funding
- Schweizerischer Nationalfonds [200021_182400, 200021_172950-1]
- Kekule Ph.D. fellowship of the Fonds der Chemischen Industrie
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Computational studies of chemical reactions in complex environments such as proteins, nanostructures, or on surfaces require accurate and efficient atomistic models applicable to the nanometer scale. In general, an accurate parametrization of the atomistic entities will not be available for arbitrary system classes but demands a fast, automated, system-focused parametrization procedure to be quickly applicable, reliable, flexible, and reproducible. Here, we develop and combine an automatically parametrizable quantum chemically derived molecular mechanics model with machine-learned corrections under autonomous uncertainty quantification and refinement. 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 of the force constants. This model is then the starting point to generate a large number of configurations for which additional off minimum reference data can be evaluated on the fly. A Delta-machine learning model is trained on these data to provide a correction to energies and forces including uncertainty estimates. During the procedure, the flexibility of the machine learning model is tailored to the amount of available training data. The parametrization of large systems is enabled by a fragmentation approach. Due to their modular nature, all model construction steps allow for model improvement in a rolling fashion. 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.
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