Machine learning shortcuts advanced materials design

January 07, 2021 //By Rich Pell
Machine learning shortcuts advanced materials design
Researchers at Sandia National Laboratories say they have successfully used machine learning to complete cumbersome materials science calculations more than 40,000 times faster than normal.

Their results, say the researchers, could herald a dramatic acceleration in the creation of new technologies for optics, aerospace, energy storage, and potentially medicine while simultaneously saving laboratories money on computing costs.

"We're shortening the design cycle," says David Montes de Oca Zapiain, a computational materials scientist at Sandia who helped lead the research. "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."

The researchers used machine learning to accelerate a computer simulation that predicts how changing a design or fabrication process, such as tweaking the amounts of metals in an alloy, will affect a material. A project might require thousands of simulations, which can take weeks, months or even years to run.

In their work, the researchers clocked a single, unaided simulation on a high-performance computing cluster with 128 processing cores at 12 minutes. With machine learning, the same simulation took 60 milliseconds using only 36 cores - equivalent to 42,000 times faster on equal computers. This, say the researchers, means that they can now learn in under 15 minutes what would normally take a year.

The researchers' new algorithm arrived at an answer that was 5% different from the standard simulation’s result - a very accurate prediction for the team's purposes. Machine learning trades some accuracy for speed because it makes approximations to shortcut calculations.

"Our machine-learning framework achieves essentially the same accuracy as the high-fidelity model but at a fraction of the computational cost," says Sandia materials scientist Rémi Dingreville, who also worked on the project.

The researchers say they are going to use their algorithm first to research ultrathin optical technologies for next-generation monitors and screens. Their research, though, could prove widely useful


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