Files in this item

FilesDescriptionFormat

application/pdf

application/pdfZHANG-DISSERTATION-2016.pdf (9MB)Restricted to U of Illinois
(no description provided)PDF

Description

Title:Database optimization algorithm for empirical potentials
Author(s):Zhang, Pinchao
Director of Research:Trinkle, Dallas R.
Doctoral Committee Chair(s):Trinkle, Dallas R.
Doctoral Committee Member(s):Bellon, Pascal; Chen, Yuguo; Ferguson, Andrew
Department / Program:Materials Science & Engineerng
Discipline:Materials Science & Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(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.
Issue Date:2016-03-01
Type:Thesis
URI:http://hdl.handle.net/2142/90714
Rights Information:Copyright 2016 Pinchao Zhang
Date Available in IDEALS:2016-07-07
Date Deposited:2016-05


This item appears in the following Collection(s)

Item Statistics