University of Illinois Urbana-Champaign

Database optimization algorithm for empirical potentials

Zhang, Pinchao

Content Files
ZHANG-DISSERTATION-2016.pdf

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https://hdl.handle.net/2142/90714

Description

Title
Database optimization algorithm for empirical potentials
Author(s)
Zhang, Pinchao
Issue Date
2016-03-01
Director of Research (if dissertation) or Advisor (if thesis)
Trinkle, Dallas R.
Doctoral Committee Chair(s)
Trinkle, Dallas R.
Committee Member(s)
  • Bellon, Pascal
  • Chen, Yuguo
  • Ferguson, Andrew
Department of Study
Materials Science & Engineerng
Discipline
Materials Science & Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2016-07-07T20:26:45Z
Keyword(s)
  • Empirical potential models
  • Optimization algorithm
  • Bayesian statistics
  • Monte Carlo
  • Genetic algorithm
  • Titanium oxygen interaction
Abstract
The development for accurate and efficient empirical potential models requires years of efforts and is highly intuitional. This study provides an automated, quantitative algorithm to find the optimal empirical potential model for a pre-determined testing set of desired structure properties. We employ Bayesian sampling technique to estimate the errors for the structural property functions in the testing set. We provide the first analytical derivations of how modifications in the fitting database affect the testing set errors. A new binary modified embedded-atom method functional form is developed for Ti-O interactions where O is in the dilute limit. The optimal Ti-O potential are tested against a variety of structure properties to verify the transferability of the potential. We propose and optimize two types of objective functions which measures the transferability in the testing set. One aims to minimize the relative errors of different fitting databases for the testing set, and the other uses the logistic function in classification regression analysis to categorize the prediction errors in the testing set into good and bad ones. We develop a parallelized genetic algorithm to efficiently evaluate the objective function and perform global search for the optimal empirical potential model.
Graduation Semester
2016-05
Type of Resource
text
Permalink
http://hdl.handle.net/2142/90714
Copyright and License Information
Copyright 2016 Pinchao Zhang

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