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Generating Information-Rich High-Throughput Experimental Materials Genomes using Functional Clustering via Multitree Genetic Programming and Information Theory

Suram, Santosh K. and Haber, Joel A. and Jin, Jian and Gregoire, John M. (2015) Generating Information-Rich High-Throughput Experimental Materials Genomes using Functional Clustering via Multitree Genetic Programming and Information Theory. ACS Combinatorial Science, 17 (4). pp. 224-233. ISSN 2156-8952. https://resolver.caltech.edu/CaltechAUTHORS:20150309-091323359

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Abstract

High-throughput experimental methodologies are capable of synthesizing, screening and characterizing vast arrays of combinatorial material libraries at a very rapid rate. These methodologies strategically employ tiered screening wherein the number of compositions screened decreases as the complexity, and very often the scientific information obtained from a screening experiment, increases. The algorithm used for down-selection of samples from higher throughput screening experiment to a lower throughput screening experiment is vital in achieving information-rich experimental materials genomes. The fundamental science of material discovery lies in the establishment of composition–structure–property relationships, motivating the development of advanced down-selection algorithms which consider the information value of the selected compositions, as opposed to simply selecting the best performing compositions from a high throughput experiment. Identification of property fields (composition regions with distinct composition-property relationships) in high throughput data enables down-selection algorithms to employ advanced selection strategies, such as the selection of representative compositions from each field or selection of compositions that span the composition space of the highest performing field. Such strategies would greatly enhance the generation of data-driven discoveries. We introduce an informatics-based clustering of composition-property functional relationships using a combination of information theory and multitree genetic programming concepts for identification of property fields in a composition library. We demonstrate our approach using a complex synthetic composition-property map for a 5 at. % step ternary library consisting of four distinct property fields and finally explore the application of this methodology for capturing relationships between composition and catalytic activity for the oxygen evolution reaction for 5429 catalyst compositions in a (Ni–Fe–Co–Ce)O_x library.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1021/co5001579DOIArticle
http://pubs.acs.org/doi/abs/10.1021/co5001579PublisherArticle
http://pubs.acs.org/doi/suppl/10.1021/co5001579PublisherSupporting Information
ORCID:
AuthorORCID
Suram, Santosh K.0000-0001-8170-2685
Haber, Joel A.0000-0001-7847-5506
Gregoire, John M.0000-0002-2863-5265
Additional Information:© 2015 American Chemical Society. Received: October 9, 2014; Revised: February 23, 2015; Publication Date (Web): February 23, 2015. This work is performed by the Joint Center for Artificial Photosynthesis, a DOE Energy Innovation Hub, supported through the Office of Science of the U.S. Department of Energy under Award Number DE-SC000499. This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. The authors thank Dr. Misha Z. Pesenson for helpful discussions.
Group:JCAP
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0004993
Department of Energy (DOE)DE-AC02-05CH11231
Issue or Number:4
Record Number:CaltechAUTHORS:20150309-091323359
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20150309-091323359
Official Citation:Generating Information-Rich High-Throughput Experimental Materials Genomes using Functional Clustering via Multitree Genetic Programming and Information Theory Santosh K. Suram, Joel A. Haber, Jian Jin, and John M. Gregoire ACS Combinatorial Science 2015 17 (4), 224-233 DOI: 10.1021/co5001579
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:55626
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:10 Mar 2015 19:43
Last Modified:09 Mar 2020 13:18

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