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

Imagine earning your engineering degree in 50 minutes? 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. 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.