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ReaxFF Parameter Optimization with Monte Carlo and Evolutionary Algorithms: Guidelines and Insights

revised on 31.07.2019 and posted on 31.07.2019 by Ganna Shchygol, Alexei Yakovlev, Tomáš Trnka, Adri C.T. van Duin, Toon Verstraelen
ReaxFF is a computationally efficient force field to simulate complex reactive dynamics in extended molecular models with diverse chemistries, if reliable force-field parameters are available for the chemistry of interest. If not, they must be optimized by minimizing the error ReaxFF makes on a relevant training set. Because this optimization is far from trivial, many methods, in particular genetic algorithms (GAs), have been developed to search for the global optimum in parameter space. Recently, two alternative parameter calibration techniques were proposed, i.e. Monte-Carlo Force Field optimizer (MCFF) and Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). In this work, CMA-ES, MCFF and a GA method (OGOLEM) are systematically compared using three training sets from the literature. GA shows the smallest risk of getting trapped into a local minimum, whereas CMA-ES is capable of reaching the lowest errors for two third of the cases. For each method, we provide reasonable default settings and our analysis offers useful guidelines for their usage in future work. An important side effect impairing the parameter optimization is numerical noise. A detailed analysis reveals that it can be reduced, e.g. by using exclusively unambiguous geometry optimizations in the training set. Even without this noise, many distinct near-optimal parameter vectors can be found, which opens new avenues for improving the training set and detecting overfitting artifacts.


Horizon 2020 programme, grant agreements No. 764810 (S4CE), 739746 (NET) and 641887 (DEFNET). Research Board of Ghent University (BOF). Computational resources and services provided by the VSC (Flemish Supercomputer Center), funded by the FWO (Research Foundation Flanders)


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Ghent University



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Declaration of Conflict of Interest

Authors G.S., A.Y. and T.T. were employed by SCM, a company developing a commercial implementation of ReaxFF and the parameter optimization algorithms discussed in this work.

Version Notes

Major revision, including supporting information