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 funne...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.展开更多
基金This work was supported by the Hartree National Centre for Digital Innovation,a collaboration between Science and Technology Facilities Council and IBM.
文摘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.
