Global optimization of parameters in the reactive force field ReaxFF for SiOH

TL;DR

This paper presents a method using genetic algorithms for global optimization of ReaxFF parameters for SiOH, achieving smaller error measures and simplifying force-field fitting.

Contribution

It introduces a parallelized genetic algorithm approach with a systematic range selection process for optimizing reactive force fields.

Findings

Optimized force fields with smaller error measures than previous work.

Using suitable parameter ranges is more critical than GA tuning.

The approach enables more accessible force-field fitting for reactive systems.

Abstract

We have used unbiased global optimization to fit a reactive force field to a given set of reference data. Specifically, we have employed genetic algorithms (GA) to fit ReaxFF to SiOH data, using an in-house GA code that is parallelized across reference data items via the message-passing interface (MPI). Details of GA tuning turn out to be far less important for global optimization efficiency than using suitable ranges within which the parameters are varied. To establish these ranges, either prior knowledge can be used or successive stages of GA optimizations, each building upon the best parameter vectors and ranges found in the previous stage. We finally arrive at optimized force fields with smaller error measures than those published previously. Hence, this optimization approach will contribute to converting force-field fitting from a specialist task to an everyday commodity, even for the more difficult case of reactive force fields.