Document Type

Book Chapter

Publication Version

Accepted Manuscript

Publication Date

2013

Journal or Book Title

Informatics for Materials Science and Engineering

First Page

349

Last Page

364

DOI

10.1016/B978-0-12-394399-6.00014-X

Abstract

We exemplify and propose extending the use of genetic programs (GPs) – a genetic algorithm (GA) that evolves computer programs via mechanisms similar to genetics and natural selection – to symbolically regress key functional relationships between materials data, especially from electronic structure. GPs can extract structure–property relations or enable simulations across multiple scales of time and/or length. Uniquely, GP-based regression permits “data discovery” – finding relevant data and/or extracting correlations (data reduction/data mining) – in contrast to searching for what you know, or you think you know (intuition). First, catalysis-related materials correlations are discussed, where simple electronic-structure-based rules are revealed using well-developed intuition, and then, after introducing the concepts, GP regression is used to obtain (i) a constitutive relation between flow stress and strain rate in aluminum, and (ii) multi-time-scale kinetics for surface alloys. We close with some outlook for a range of applications (materials discovery, excited-state chemistry, and multiscaling) that could rely primarily on density functional theory results.

Comments

This is a manuscript of a book chapter from Informatics for Materials Science and Engineering (2013, Elsevier), edited by Krishna Rajan, doi:10.1016/B978-0-12-394399-6.00014-X. Posted with permission.

Copyright Owner

Elsevier Inc.

Language

en

File Format

application/pdf

Published Version

Share

COinS