Cost-effective materials discovery: Bayesian optimization across multiple information sources-Reference-Cited by-同舟云学术

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

Published:2020 Issue:8 Volume:7 Page:2113-2123
ISSN:2051-6347
Container-title:Materials Horizons
language:en
Short-container-title:Mater. Horiz.

Author:

Herbol Henry C.¹²³⁴^ORCID,Poloczek Matthias⁵⁶⁴,Clancy Paulette¹²³⁴^ORCID

Affiliation:

1. Department of Chemical and Biomolecular Engineering

2. Johns Hopkins University

3. Baltimore

4. USA

5. Uber AI

6. San Francisco

Abstract

Multi-information source Bayesian optimization and how it can be used to capture relevant information from cheap approximations to accelerate research in the materials sciences.

Funder

National Science Foundation

Whiting School of Engineering, Johns Hopkins University

Publisher

Royal Society of Chemistry (RSC)

Subject

Electrical and Electronic Engineering,Process Chemistry and Technology,Mechanics of Materials,General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2020/MH/D0MH00062K

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