Multi-objective parametrization of interatomic potentials for large deformation pathways and fracture of two-dimensional materials

Xu Zhang, Hoang Nguyen, Jeffrey T. Paci, Subramanian K.R.S. Sankaranarayanan, Jose L. Mendoza-Cortes, Horacio D. Espinosa*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This investigation presents a generally applicable framework for parameterizing interatomic potentials to accurately capture large deformation pathways. It incorporates a multi-objective genetic algorithm, training and screening property sets, and correlation and principal component analyses. The framework enables iterative definition of properties in the training and screening sets, guided by correlation relationships between properties, aiming to achieve optimal parametrizations for properties of interest. Specifically, the performance of increasingly complex potentials, Buckingham, Stillinger-Weber, Tersoff, and modified reactive empirical bond-order potentials are compared. Using MoSe2 as a case study, we demonstrate good reproducibility of training/screening properties and superior transferability. For MoSe2, the best performance is achieved using the Tersoff potential, which is ascribed to its apparent higher flexibility embedded in its functional form. These results should facilitate the selection and parametrization of interatomic potentials for exploring mechanical and phononic properties of a large library of two-dimensional and bulk materials.

Original languageEnglish (US)
Article number113
Journalnpj Computational Materials
Volume7
Issue number1
DOIs
StatePublished - Dec 2021

ASJC Scopus subject areas

  • Modeling and Simulation
  • Materials Science(all)
  • Mechanics of Materials
  • Computer Science Applications

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