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Phase Mapper: Accelerating Materials Discovery with AI

Bai, Junwen and Xue, Yexiang and Bjorck, Johan and Le Bras, Ronan and Rappazzo, Brendan and Bernstein, Richard and Suram, Santosh K. and van Dover, Robert Bruce and Gregoire, John M. and Gomes, Carla P. (2018) Phase Mapper: Accelerating Materials Discovery with AI. AI Magazine, 39 (1). pp. 15-26. ISSN 0738-4602. https://resolver.caltech.edu/CaltechAUTHORS:20180711-161340753

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Abstract

From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1609/aimag.v39i1.2785 DOIArticle
ORCID:
AuthorORCID
Suram, Santosh K.0000-0001-8170-2685
Gregoire, John M.0000-0002-2863-5265
Additional Information:© 2018 Association for the Advancement of Artificial Intelligence.
Issue or Number:1
Record Number:CaltechAUTHORS:20180711-161340753
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20180711-161340753
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:87783
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:11 Jul 2018 23:33
Last Modified:03 Oct 2019 19:59

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