A multi-fidelity machine learning approach to high throughput materials screening

Fare, Clyde, Fenner, Peter, Benatan, Matthew, Varsi, Alessandro ORCID: 0000-0003-2218-4720 and Pyzer-Knapp, Edward OO ORCID: 0000-0002-8232-8282 (2022) A multi-fidelity machine learning approach to high throughput materials screening. NPJ COMPUTATIONAL MATERIALS, 8 (1). 257-.

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Abstract

<jats:title>Abstract</jats:title><jats:p>The ever-increasing capability of computational methods has resulted in their general acceptance as a key part of the materials design process. Traditionally this has been achieved using a so-called computational funnel, where increasingly accurate - and expensive – methodologies are used to winnow down a large initial library to a size which can be tackled by experiment. In this paper we present an alternative approach, using a multi-output Gaussian process to fuse the information gained from both experimental and computational methods into a single, dynamically evolving design. Common challenges with computational funnels, such as mis-ordering methods, and the inclusion of non-informative steps are avoided by learning the relationships between methods on the fly. We show this approach reduces overall optimisation cost on average by around a factor of three compared to other commonly used approaches, through evaluation on three challenging materials design problems.</jats:p>

Item Type:	Article
Uncontrolled Keywords:	Networking and Information Technology R&D (NITRD), Generic health relevance
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	02 Mar 2023 08:32
Last Modified:	15 Mar 2024 13:15
DOI:	10.1038/s41524-022-00947-9
Open Access URL:	https://doi.org/10.1038/s41524-022-00947-9
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3168668