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

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Suram:2015:ACS,
  author =       "Santosh K. Suram and Joel A. Haber and Jian Jin and 
                 John M. Gregoire",
  title =        "Generating Information-Rich High-Throughput
                 Experimental Materials Genomes using Functional
                 Clustering via Multitree Genetic Programming and
                 Information Theory",
  journal =      "ACS Combinatorial Science",
  volume =       "17",
  number =       "4",
  pages =        "224--233",
  publisher =    "American Chemical Society",
  year =         "2015",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "2156-8952",
  bibsource =    "OAI-PMH server at authors.library.caltech.edu",
  oai =          "oai:authors.library.caltech.edu:55626",
  type =         "PeerReviewed",
  URL =          "http://authors.library.caltech.edu/55626/2/co5001579_si_001.pdf",
  notes =        "http://resolver.caltech.edu/CaltechAUTHORS:20150309-091323359;
                 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.
                 http://resolver.caltech.edu/CaltechAUTHORS:20150309-091323359
                 ;
                 ; http://authors.library.caltech.edu/55626/",
  abstract =     "High-throughput experimental methodologies are capable
                 of synthesising, screening and characterising 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. percent 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.",
}

Genetic Programming entries for Santosh K Suram Joel A Haber Jian Jin John M Gregoire

Citations