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

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.

Automated summary

The increasing importance of computational methods in materials design is addressed in this paper. An alternative approach using a multi-output Gaussian process to combine experimental and computational methods into a dynamically evolving design is presented. Common challenges with traditional computational funnels are avoided by learning the relationships between methods in real time. Evaluation on three challenging materials design problems shows that this approach reduces overall optimization cost by an average factor of three compared to other commonly used methods.