Boosting ReaxFF Reactive Force Field Optimization with Adaptive Sampling

S Li and SY Yang and SB Chen and W Zheng and ZJ Dong and LL Luo and WW Zhang and X Chen, JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 21, 4652-4660 (2025).

DOI: 10.1021/acs.jctc.4c01748

In ReaxFF reactive force field conventional optimizations, the quality of the initial guesses plays a crucial role in determining the accuracy of the parametrization, particularly in high-dimensional spaces. To address this, we propose an adaptive sampling method that efficiently identifies high-quality initial guesses through uniform sampling followed by iterative refinement. Using this framework, we applied three optimization approaches to parametrize the Cu/H/O ReaxFF force field. The developed force field was used to study copper surface reconstruction with water molecules, revealing a stable bilayer structure driven by OH intrusion, which aligns closely with experimental observations. This adaptive sampling approach serves as a powerful tool for efficiently developing reliable ReaxFF reactive force field, enabling high-precision modeling of chemical reactions at the nanoscale.

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