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Getting from the computer to real world materials faster with machine learning

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If you have a question about this talk, please contact Dr Fabian Berger .

I will describe our efforts to accelerate the discovery of novel transition metal containing materials using machine learning. I will discuss how we have leveraged experimental data sets through both text mining and semantic embedding to uncover relationships between structure and function. Then I will describe how we have leveraged large datasets of synthesized materials to uncover those with novel function in polymer networks. I will describe how we demonstrate the success of our design strategy through macroscopically visible changes in network scale properties.

This talk is part of the Lennard-Jones Centre series.

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