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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 [email protected] and Mark A. Tschopp at [email protected]
Address all correspondence to Efraín Hernández-Rivera at [email protected] and Mark A. Tschopp at [email protected]
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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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References

1.Benedyk, J.: Aluminum Alloys for Lightweight Automotive Structures in Materials, Design and Manufacturing for Lightweight Vehicles (Woodhead Publishing Series, Cambridge, 2010).Google Scholar
2.Joost, W.: Reducing vehicle weight and improving U.S. energy efficiency using integrated computational materials engineering. JOM 64, 1032 (2012).Google Scholar
3.Flower, H.M.: High Performance Materials in Aerospace (Springer Science & Business Media, London, 2012).Google Scholar
4.McCauley, J., Crowson, A., Gooch, W., Rajendran, A., Bless, S., Logan, K., Normandia, M., and Wax, S.: Ceramic Armor Materials by Design (John Wiley & Sons, Maui, 2012).Google Scholar
5.Coleman, S.P., Hernandez-Rivera, E., Behler, K.D., Synowczynski-Dunn, J., and Tschopp, M.A: Challenges of engineering grain boundaries in boron-based armor ceramics. JOM 68, 1605 (2016).Google Scholar
6.Pollock, T.M.: Weight loss with magnesium alloys. Science 328, 986 (2010).Google Scholar
7.Domnich, V., Reynaud, S., Haber, R.A., and Chhowalla, M.: Boron carbide: structure, properties, and stability under stress. JACerS 94, 3605 (2011).Google Scholar
8.Kaufmann, C., Cronin, D., Worswick, M., Pageau, G., and Beth, A.: Influence of material properties on the ballistic performance of ceramics for personal body armour. Shock Vib. 10, 51 (2003).Google Scholar
9.Thevenot, F.: Boron carbide – a comprehensive review. J. Euro. Cer. Soc. 6, 205 (1990).Google Scholar
10.Chen, M., McCauley, J.W., and Hemker, K.J.: Shock-induced localized amorphization in boron carbide. Science 299, 1563 (2003).Google Scholar
11.Reddy, K.M., Liu, P., Hirata, A., Fujita, T., and Chen, M.W.: Atomic structure of amorphous shear bands in boron carbide. Nature Comms. 4, 2483 (2013).Google Scholar
12.Horstemeyer, M.F.: Integrated Computational Materials Engineering (ICME) for Metals: Using Multiscale Modeling to Invigorate Engineering Design with Science (John Wiley & Sons, Hoboken, 2012).Google Scholar
13.Plimpton, S.J. and Thompson, A.P.: Computational aspects of many-body potentials. MRS Bull. 37, 513 (2012).Google Scholar
14.van Duin, A.C.T., Dasgupta, S., Lorant, F., and Goddard, W.A.: ReaxFF: a reactive force field for hydrocarbons. J. Phys. Chem. A 105, 9396 (2001).Google Scholar
15.Brenner, D.W., Shenderova, O.A., Harrison, J.A., Stuart, S.J., Ni, B., and Sinnott, S.B.: A second-generation reactive empirical bond order (REBO) potential energy expression for hydrocarbons. J. Phys.: Cond. Matt. 14, 783 (2002).Google Scholar
16.Yu, J., Sinnott, S.B., and Phillpot, S.R.: Charge optimized many-body potential for the Si/SiO2 system. Phys. Rev. B 75, 085311 (2007).Google Scholar
17.An, Q., and Goddard, W.A. III: Atomistic Origin of Brittle Failure of Boron Carbide From Large-Scale Reactive Dynamics Simulations: Suggestions Toward Improved Ductility. Phys. Rev. Letts. 115, 105501 (2015).Google Scholar
18.Jaramillo-Botero, A., Naserifar, S., and Goddard, W.A. III: General multiobjective force field optimization framework, with application to reactive force fields for silicon carbide. J. Chem. Theo. Comp. 10, 1426 (2014).Google Scholar
19.Rice, B.M., Larentzos, J.P., Byrd, E.F., and Weingarten, N.S.: Parameterizing complex reactive force fields using Multiple Objective Evolutionary Strategies (MOES): Part 2: transferability of ReaxFF models to C-H-N-O energetic materials: J. Chem. Theo. Comp. 11, 392 (2015).Google Scholar
20.Martinez, J.A., Chernatynskiy, A., Yilmaz, D.E., Liang, T., Sinnott, S.B., and Phillpot, S.R.: Potential optimization software for materials (POSMat). Comp. Phys. Comms. 203, 201 (2016).Google Scholar
21.Tschopp, M.A., Solanki, K., Baskes, M.I., Gao, F., Sun, X., and Horstemeyer, M.F.: Generalized framework for interatomic potential design: application to Fe-He system. J. Nuc. Mats. 425, 22 (2012).Google Scholar
22.Gosset, D. and Colin, M.: Boron carbides of various compositions: an improved method for X-rays characterisation. J. Nuc. Mats. 183, 161 (1991).Google Scholar
23.Fanchini, G., McCauley, J.W., and Chhowalla, M.: Behavior of disordered boron carbide under stress. Phys. Rev. Letts. 97, 035502 (2006).Google Scholar
24.Taylor, D.E., McCauley, J.W., and Wright, T.W.: The effects of stoichiometry on the mechanical properties of icosahedral boron carbide under loading. J. Phys.: Cond. Matter 24, 505402 (2012).Google Scholar
25.Paupitz, R., Junkermeier, C.E., van Duin, A.C.T., and Branicio, P.S.: Fullerenes generated from porous structures. Phys. Chem. Chem. Phys. 16, 25515 (2014).Google Scholar
26.Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global Sensitivity Analysis: The Primer (John Wiley & Sons, New York, 2008).Google Scholar
27.Sobol, I.M. and Kucherenko, S.: Derivative based global sensitivity measures and their links with global sensitivity indices. Math. Comps. Sim. 79, 3009 (2009).Google Scholar
28.Kucherenko, S. and Iooss, B.: Derivative-Based Global Sensitivity Measures in Handbook of Uncertainty Quantification (Springer International Publishing, Berlin, 2016).Google Scholar
29.Herman, J. and Usher, W.: SALib: an open-source Python library for sensitivity analysis. J. Open Source Software 2, 97 (2017).Google Scholar
30.Becker, W.E., Tarantola, S., and Deman, G.: Sensitivity analysis approaches to high- dimensional screening problems at low sample size. J. Stat. Comp. Sim. 88, 2089 (2018).Google Scholar
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