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High Throughput Combinatorial Experimentation + Informatics = Combinatorial Science

Suram, Santosh K. and Pesenson, Meyer Z. and Gregoire, John M. (2015) High Throughput Combinatorial Experimentation + Informatics = Combinatorial Science. In: Information Science for Materials Discovery and Design. Springer Series in Materials Science. No.225. Springer , Cham, Switzerland, pp. 271-300. ISBN 978-3-319-23870-8. https://resolver.caltech.edu/CaltechAUTHORS:20170718-103549107

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Abstract

Many present, emerging and future technologies rely upon the development high performance functional materials. For a given application, the performance of materials containing 1 or 2 elements from the periodic table have been evaluated using traditional techniques, and additional materials complexity is required to continue the development of advanced materials, for example through the incorporation of several elements into a single material. The combinatorial aspect of combining several elements yields vast composition spaces that can be effectively explored with high throughput techniques. State of the art high throughput experiments produce data which are multivariate, high-dimensional, and consist of wide ranges of spatial and temporal scales. We present an example of such data in the area of water splitting electrocatalysis and describe recent progress on 2 areas of interpreting such vast, complex datasets. We discuss a genetic programming technique for automated identification of composition-property trends, which is important for understanding the data and crucial in identifying representative compositions for further investigation. By incorporating such an algorithm in a high throughput experimental pipeline, the automated down-selection of samples can empower a highly efficient tiered screening platform. We also discuss some fundamental mathematics of composition spaces, where compositional variables are non-Euclidean due to the constant-sum constraint. We describe the native simplex space spanned by composition variables and provide illustrative examples of statistics and interpolation within this space. Through further development of machine learning algorithms and their prudent implementation in the simplex space, the data informatics community will establish methods that derive the most knowledge from high throughput materials science data.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://dx.doi.org/10.1007/978-3-319-23871-5_14DOIArticle
https://link.springer.com/chapter/10.1007%2F978-3-319-23871-5_14PublisherArticle
http://rdcu.be/Cn0bPublisherFree ReadCube access
ORCID:
AuthorORCID
Suram, Santosh K.0000-0001-8170-2685
Gregoire, John M.0000-0002-2863-5265
Additional Information:© 2016 Springer International Publishing Switzerland. First Online: 13 December 2015. The authors would like to thank Prof. Alfred Ludwig for stimulating discussions. 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.
Group:JCAP
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC000499
Series Name:Springer Series in Materials Science
Issue or Number:225
Record Number:CaltechAUTHORS:20170718-103549107
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170718-103549107
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:79151
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:18 Jul 2017 18:02
Last Modified:03 Oct 2019 18:16

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