Global optimization of parameters in the reactive force field ReaxFF for SiOH
This work addresses the challenge of force-field fitting for reactive systems, potentially converting it from a specialist task to an everyday commodity, though it appears incremental in method.
The researchers tackled the problem of fitting reactive force fields (ReaxFF) to SiOH data by using unbiased global optimization with genetic algorithms, resulting in optimized force fields with smaller error measures than previously published ones.
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.