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Machine Learning Could Lop a Year Off Technology Design Cycle

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A research team at Sandia National Laboratories has successfully used machine learning to complete cumbersome materials science calculations more than 40,000 times faster than normal.

Their results 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.

The team describes its work in "Accelerating Phase-Field-Based Microstructure Evolution Predictions via Surrogate Models Trained by Machine Learning Methods," published in npj Computational Materials.

"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."

Sandia researchers used machine learning to accelerate a computer simulation that predicts how changing a design or fabrication process will affect a material. A project might require thousands of simulations.

The team 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 36 cores.

"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.

From Sandia National Laboratories
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