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INDEEDopt_chemrxiv_v2.pdf (1.02 MB)

An Initial Design-enhanced Deep Learning-based Optimization Framework to Parameterize Multicomponent ReaxFF Force Fields

revised on 18.06.2020, 19:50 and posted on 22.06.2020, 07:48 by Mert Sengul, Yao Song, Nadire Nayir, Yawei Gao, Ying Hung, Tirthankar Dasgupta, Adri C.T. van Duin

ReaxFF is an empirical interatomic potential capable of simulating reactions in complex chemical processes and thus determine the dynamical evolution of the molecular systems. A drawback of this method is the necessity of a significant preprocessing to adapt it to a chemical system of nterest. One of the preprocessing steps is the optimization of force field parameters that are used to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of these parameters is a very complex high dimensional problem. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to be used in ReaxFF parameterization. The procedure starts with a Latin Hypercube Design (LHD) algorithm that is used to explore the parameter landscape extensively. The LHD passes the information about explored regions to a deep learning model for training. The deep learning model finds the minimum discrepancy regions and eliminates unfeasible regions, which originate from the unphysical atomistic interactions, and constructs a more comprehensive understanding of a physically meaningful parameter space. The procedure was successfully used to parametrize a nickel-chromium binary force field and a tungsten-sulfide-carbon-hydrogen quaternary force field and produced improved accuracies in shorter periods time compared to conventional optimization method.


U.S. NSF DMR-1842922

U.S. NSF DMR-1842952

U.S. NSF MRI-1626251

U.S. NSF DMR-1539916


Email Address of Submitting Author


The Pennsylvania State Unviersity


United States of America

ORCID For Submitting Author

0000-0002- 5309-0316

Declaration of Conflict of Interest

No conflict of interest

Version Notes

Version 1.0