Multiobjective Genetic-Algorithm Workflow for Training and Uncertainty Quantification of Reactive Molecular-Dynamics Force Fields*

The conventional approach of training a ReaxFF reactive force field parameters by fitting to a quantum-mechanical database is tedious and time consuming, and requires a considerable degree of prior experience. For fast and automated development of ReaxFF force fields, we propose a dynamic approach of directly fitting ReaxFF based reactive molecular dynamics (RMD) trajectories against a quantum molecular dynamics (QMD) trajectory on the fly. Here, we present a scalable in situ MOGA (iMOGA) workflow that eliminates the file I/O bottleneck involving file base communication between RMD, QMD and genetic algorithm computations, using inter-process communications but with minimal modification of the original parallel RMD code. The iMOGA workflow has been used to tune ReaxFF parameters for the sulfidation of MoO₃ by H₂S, while providing uncertainty quantification (UQ) of the force field.