Optimization of ReaxFF parameters for the Mo−S system using random optimization and coordinate search
Abstract
ReaxFF is a molecular dynamics method that can be considered a good approximation to quantum methods for investigating reactive molecular systems consisting of ten thousand to one hundred thousand atoms. While ReaxFF is usually a much faster alternative to quantum methods, the force field consists of nearly 100 parameters per element, which makes the force field development a high dimensional optimization problem. In addition to the high-dimensionality, non-convexity and non-continuity make it a hard problem to optimize. We use random optimization along with coordinate search strategies to optimize efficiently and sample new parameter points that yield good molecular properties close to predefined `reference values' obtained from quantum mechanical methods for the Mo−S system. We also provide empirical error guaranties starting from any random sample of inputs. We discover new points for the Mo−S system at adjusted error levels of 13,000 as compared to Sengul et al. (2022) at 70,000 levels under the same loss function, registering over 80% improvement. We also extend our algorithm to an out-of-sample system, W−S, with no training data to record over 70% improvement over Sengul et al. (2021).
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BibTeX
@misc{ravichandran2026optimization,
title = {{Optimization of ReaxFF parameters for the $\mathrmMo-S$ system using random optimization and coordinate search}},
author = {Arun Ravichandran and Michael L. Stein and Mert Y. Sengul and Ying Hung and Tirthankar Dasgupta},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.12401},
eprint = {2609.12401},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.12401},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Optimization of ReaxFF parameters for the Mo−S system using random optimization and coordinate search
AU - Arun Ravichandran
AU - Michael L. Stein
AU - Mert Y. Sengul
AU - Ying Hung
AU - Tirthankar Dasgupta
PY - 2026
DA - 2026/09/15
PB - arXiv
DO - 10.48550/arxiv.2609.12401
UR - https://arxiv.org/abs/2609.12401
AB - ReaxFF is a molecular dynamics method that can be considered a good approximation to quantum methods for investigating reactive molecular systems consisting of ten thousand to one hundred thousand atoms. While ReaxFF is usually a much faster alternative to quantum methods, the force field consists of nearly 100 parameters per element, which makes the force field development a high dimensional optimization problem. In addition to the high-dimensionality, non-convexity and non-continuity make it a hard problem to optimize. We use random optimization along with coordinate search strategies to optimize efficiently and sample new parameter points that yield good molecular properties close to predefined `reference values' obtained from quantum mechanical methods for the Mo−S system. We also provide empirical error guaranties starting from any random sample of inputs. We discover new points for the Mo−S system at adjusted error levels of 13,000 as compared to Sengul et al. (2022) at 70,000 levels under the same loss function, registering over 80% improvement. We also extend our algorithm to an out-of-sample system, W−S, with no training data to record over 70% improvement over Sengul et al. (2021).
ER -