Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation-Reference-Cited by-同舟云学术

Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation

Published:2023 Issue:46 Volume:25 Page:32123-32131
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

McGuinness Kevin P.¹^ORCID,Oliynyk Anton O.²^ORCID,Lee Sangjoon³,Molero-Sanchez Beatriz¹^ORCID,Addo Paul Kwesi¹^ORCID

Affiliation:

1. SeeO2 Energy Inc., 3655 36 St NW, Calgary, AB T2L 1Y8, Canada

2. Department of Chemistry, Hunter College, City University of New York, New York, NY, 10065, USA

3. Department of Applied Physics and Applied Mathematics, Columbia University, New York 10027, New York, USA

Abstract

The support vector machine model produced the best results with a root mean square error of 1.54 × 10−6 K−1. The model was applied to 3 593 726 possible AA′BB′O3 compositions, resulting in 150 451 predictions in the model confidence region.

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D3CP04017H

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