Self-Parametrizing System-Focused Atomistic Models

TL;DR

This paper introduces an automated, system-focused atomistic modeling approach combining quantum chemistry, machine learning corrections, and uncertainty quantification to enable accurate and flexible simulations of complex nanostructures.

Contribution

It develops a novel, automated parametrization method that integrates quantum chemistry, machine learning, and fragmentation for efficient modeling of complex systems.

Findings

Accurate models generated from minimal energy structures and Hessians.

Machine learning corrections improve energy and force predictions with uncertainty estimates.

Modular approach allows iterative model refinement and application to large systems.

Abstract

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.