npj Computational Materials (May 2021)

INDEEDopt: a deep learning-based ReaxFF parameterization framework

  • Mert Y. Sengul,
  • Yao Song,
  • Nadire Nayir,
  • Yawei Gao,
  • Ying Hung,
  • Tirthankar Dasgupta,
  • Adri C. T. van Duin

DOI
https://doi.org/10.1038/s41524-021-00534-4
Journal volume & issue
Vol. 7, no. 1
pp. 1 – 9

Abstract

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Abstract Empirical interatomic potentials require optimization of force field parameters to tune interatomic interactions to mimic ones obtained by quantum chemistry-based methods. The optimization of the parameters is complex and requires the development of new techniques. Here, we propose an INitial-DEsign Enhanced Deep learning-based OPTimization (INDEEDopt) framework to accelerate and improve the quality of the 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, which finds the minimum discrepancy regions and eliminates unfeasible regions, and constructs a more comprehensive understanding of physically meaningful parameter space. We demonstrate the procedure here for the parameterization of a nickel–chromium binary force field and a tungsten–sulfide–carbon–oxygen–hydrogen quinary force field. We show that INDEEDopt produces improved accuracies in shorter development time compared to the conventional optimization method.