University of Cambridge > > Physics and Chemistry of Solids Group > Machine learning in materials design, oil exploration, and beyond

Machine learning in materials design, oil exploration, and beyond

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We have developed a new tool that employs neural networks to guide data acquisition, model trends, and exploit correlations to find optimal configurations. We exploited the tool to discover four new alloys for use in jet engines, whose properties have been experimentally verified and are now undergoing compliance testing by Rolls-Royce plc. The tool is being further developed with the support of a Samsung GRO to encompass further materials prediction tools, and with BP to guide oil exploration.

This talk is part of the Physics and Chemistry of Solids Group series.

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