While machine learning has previously been used to shortcut simulations that calculate how interactions between atoms and molecules change over time, the researchers say their results demonstrate the first use of machine learning to accelerate simulations of materials at relatively large, microscopic scales, which they expect will be of greater practical value to scientists and engineers. For instance, scientists can now quickly simulate how miniscule 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, say the researchers, including the formation of proteins, follow similar patterns. And while the researchers have not tested the machine learning algorithm on simulations of proteins, they say they are interested in exploring the possibility in the future.
The research was funded by the U.S. Department of Energy’s Basic Energy Sciences program and was conducted at the Center for Integrated Nanotechnologies, a DOE user research facility jointly operated by Sandia and Los Alamos national labs. For more, see " Accelerating phase-field-based microstructure evolution predictions via surrogate models trained by machine learning methods ."
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