Machine learning–accelerated design and synthesis of polyelemental heterostructures-Reference-Cited by-同舟云学术

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Machine learning–accelerated design and synthesis of polyelemental heterostructures

Published:2021-12-24 Issue:52 Volume:7 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Wahl Carolin B.¹²^ORCID,Aykol Muratahan³^ORCID,Swisher Jordan H.²⁴^ORCID,Montoya Joseph H.³^ORCID,Suram Santosh K.³^ORCID,Mirkin Chad A.¹²⁴^ORCID

Affiliation:

1. Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA.

2. International Institute for Nanotechnology, Northwestern University, Evanston, IL 60208, USA.

3. Toyota Research Institute, Los Altos, CA 94022, USA.

4. Department of Chemistry, Northwestern University, Evanston, IL 60208, USA.

Abstract

Machine learning accelerates materials discovery by suggesting targets, yielding exceptionally complex biphasic nanoparticles.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

Reference38 articles.

1. Surface and Interface Control in Nanoparticle Catalysis

2. Catalyst discovery through megalibraries of nanomaterials

3. Discovery of new materials using combinatorial synthesis and high-throughput characterization of thin-film materials libraries combined with computational methods

4. A Combinatorial Approach to Materials Discovery

5. Cost-effective materials discovery: Bayesian optimization across multiple information sources

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