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New Machine Learning Algorithm Makes Scientific Research 40,000 Times Faster
New Machine Learning Algorithm Makes Scientific Research 40,000 Times Faster

New Machine Learning Algorithm Makes Scientific Research 40,000 Times Faster

Imagine earning your engineering degree in 50 minutes? Or flying from New York to Las Angles in 0.5 seconds? Sandia National Laboratories has developed a new machine-learning algorithm capable of performing simulations for materials scientists nearly 40,000 times faster than normal, according to a Sandia press release.

Their results, published in the January issue of a journal called npj Computational Materials, could herald a dramatic acceleration in the development of new technologies for optics, aerospace, energy storage, and potentially medicine while simultaneously saving laboratories money on computing costs, according to the study.

The research, funded by the U.S. Department of Energy’s Basic Energy Sciences program, was conducted at the Center for Integrated Nanotechnologies, a Department of Energy user research facility jointly operated by Sandia and Los Alamos national labs.

Machine learning speeds up computationally expensive simulations

Machine learning speeds up computationally expensive simulations
Machine learning speeds up computationally expensive simulations, Image Courtesy of iStockPhotos

Sandia 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.

“We’re shortening the design cycle,” said 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 research team clocked a single, unaided simulation on a high-performance computing cluster with 128 processing cores—more than 120 more processing cores than the average home computer—at 12 minutes. With machine learning, the same simulation took only 60 milliseconds using only 36 cores, equivalent to 42,000 times faster on equal computers. This means researchers can now learn in under 15 minutes what would normally take a year.

These advancements will be like, you could eat a fresh tomato 3 minutes after planting a seed, watch all of the Game of Thrones in 6 seconds and graduate from engineering school in 50 minutes. Isn’t it amazing?

Sandia’s new algorithm arrived at an answer that was 5 percent 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.

Benefits could extend beyond materials

Benefits could extend beyond materials
Benefits could extend beyond materials, Image Courtesy of iStockPhotos

The team will use the algorithm first to research ultrathin optical technologies for next-generation monitors and screens. Their research, though, could prove widely useful because the simulation they accelerated describes a common event: the change, or evolution, of a material’s microscopic building blocks over time.

Machine learning previously has been used to shortcut simulations that calculate how interactions between atoms and molecules change over time. The published results, however, demonstrate the first use of machine learning to accelerate simulations of materials at relatively large, microscopic scales, which the Sandia team expects will be of greater practical value to scientists and engineers.

For instance, scientists can now quickly simulate how minuscule droplets of melted metal will glob together when they cool and solidify, or conversely, how a mixture will separate into layers of its constituent parts when it melts. Many other natural phenomena, including the formation of proteins, follow similar patterns. And while the Sandia team has not tested the machine-learning algorithm on simulations of proteins, they are interested in exploring the possibility in the future.

Aimal Khan
Aimal Khan is the founder & CEO of Engineering Passion. He is an engineer and has obtained his bachelor's degree in energy engineering from Kandahar University.