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Machine Learning: Predicting Material Properties

By Dick Weisinger

Scientists are increasingly pairing machine learning with physics and chemistry laws to predict physical phenomenon, like protein folding and structures, and the prediction of material properties.

A team of scientists at Sandia National Laboratories found that by using Machine Learning that they are able to make materials science calculations 40,000 times faster. Using the technique, it is possible to make calculations in 15 minutes that would have previously required a year of computations.

David Montes de Oca Zapiain, scientist at Sandia, said that “we’re shortening the design cycle. The design of components grossly outpaces the design of the materials you need to build them. We want to change that. Once you design a component, we’d like to be able to design a compatible material for that component without needing to wait for years, as it happens with the current process.”

Rémi Dingreville, Sandia materials scientist, said that “our machine-learning framework achieves essentially the same accuracy as the high-fidelity model but at a fraction of the computational cost.”

The Sandia scientists seek to apply their new approach towards the investigation of developing new ultrathin optical materials that could be used in monitors and screens.

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