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Machine Learning Algorithm Outdoes Humans
August 7, 2018

Researchers from the University of Wisconsin-Madison (UW-Madison) and Oak Ridge National Laboratory have trained computers to quickly and consistently detect and analyze microscopic radiation damage to materials under consideration for nuclear reactors. And the computers bested humans in this arduous task.

“Machine learning has great potential to transform the current, human-involved approach of image analysis in microscopy,” says Wei Li, who earned his master’s degree in materials science and engineering this year from UW-Madison.

Many problems in materials science are image-based, yet few researchers have expertise in machine vision – making image recognition and analysis a major research bottleneck. As a student, Li realized that he could leverage training in the latest computational techniques to help bridge the gap between artificial intelligence and materials science research.

Li, with Oak Ridge staff scientist Kevin Field and UW-Madison materials science and engineering professor Dane Morgan, used machine learning to make artificial intelligence better than experienced humans at analyzing damage to potential nuclear reactor materials. The collaborators described their approach in a paper published recently in the journal npj Computational Materials.

Machine learning uses statistical methods to guide computers toward improving their performance on a task without receiving any explicit guidance from a human. Essentially, machine learning teaches computers to teach themselves.

According to information, the researchers targeted machine learning as a means to rapidly sift through electron microscopy images of materials that had been exposed to radiation, and identify a specific type of damage – a challenging task because the photographs can resemble a cratered lunar surface or a splatter-painted canvas.

That job, absolutely critical to developing safe nuclear materials, could make a time-consuming process much more efficient and effective.

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