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Integrating exploratory data analytics into ReaxFF parameterization

Published online by Cambridge University Press:  18 September 2018

Efraín Hernández-Rivera*
Affiliation:
U.S. Army Research Laboratory, Weapons and Materials Research Directorate, RDRL-WMM, APG, MD 21005, USA
Souma Chowdhury
Affiliation:
Department of Mechanical and Aerospace Engineering, University at Buffalo, 246 Bell Hall, University at Buffalo, Buffalo, NY 14260, USA
Shawn P. Coleman
Affiliation:
U.S. Army Research Laboratory, Weapons and Materials Research Directorate, RDRL-WMM, APG, MD 21005, USA
Payam Ghassemi
Affiliation:
Department of Mechanical and Aerospace Engineering, University at Buffalo, 246 Bell Hall, University at Buffalo, Buffalo, NY 14260, USA
Mark A. Tschopp*
Affiliation:
U.S. Army Research Laboratory, Weapons and Materials Research Directorate, RDRL-WMM, APG, MD 21005, USA
*
Address all correspondence to Efraín Hernández-Rivera at efrain.hernandez18.civ@mail.mil and Mark A. Tschopp at mark.a.tschopp.civ@mail.mil
Address all correspondence to Efraín Hernández-Rivera at efrain.hernandez18.civ@mail.mil and Mark A. Tschopp at mark.a.tschopp.civ@mail.mil
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Abstract

We present a systematic approach to refine hyperdimensional interatomic potentials, which is showcased on the ReaxFF formulation. The objective of this research is to utilize the relationship between interatomic potential input variables and objective responses (e.g., cohesive energy) to identify and explore suitable parameterizations for the boron carbide (B–C) system. Through statistical data analytics, ReaxFF's parametric complexity was overcome via dimensional reduction (55 parameters) while retaining enough degrees of freedom to capture most of the variability in responses. Two potentials were identified which improved on an existing parameterization for the objective set if interest, showcasing the framework's capabilities.

Type
Research Letters
Copyright
Copyright © Materials Research Society 2018 

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