Database optimization for empirical interatomic potential models

Pinchao Zhang, Dallas R. Trinkle

Research output: Contribution to journalArticlepeer-review

Abstract

Weighted least squares fitting to a database of quantum mechanical calculations can determine the optimal parameters of empirical potential models. While algorithms exist to provide optimal potential parameters for a given fitting database of structures with corresponding energy-related predictions and to estimate prediction errors using Bayesian sampling, defining an optimal fitting database based on potential predictions remains elusive. A testing set of structures and energy-related predictions provides an empirical measure of potential transferability. Here, we propose an objective function for fitting databases based on testing set errors. The objective function allows the optimization of the weights in a fitting database, the assessment of the adding or removing of structures in the fitting database, or the comparison of two different fitting databases. To showcase this technique, we consider an example Lennard-Jones potential for Ti, where modeling multiple complicated crystal structures is difficult for a radial pair potential. The algorithm finds different optimal fitting databases, depending on the objective function of potential prediction error for a testing set.

Original languageEnglish (US)
Article number065011
JournalModelling and Simulation in Materials Science and Engineering
Volume23
Issue number6
DOIs
StatePublished - Sep 1 2015

Keywords

  • Bayesian sampling
  • Empirical potential
  • database optimization algorithm
  • potential transferability
  • weight optimization

ASJC Scopus subject areas

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

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