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• W4313396405 abstract "Abstract 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." @default.
• W4313396405 created "2023-01-06" @default.
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• W4313396405 date "2022-12-19" @default.
• W4313396405 modified "2023-09-26" @default.
• W4313396405 title "A multi-fidelity machine learning approach to high throughput materials screening" @default.
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• W4313396405 doi "https://doi.org/10.1038/s41524-022-00947-9" @default.
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