The 2023 IEEE Intermag Conference showcased the powerful role of machine learning in accelerating magnetic materials research and the development of new materials, marking significant strides in digital storage, efficient motors, and sustainable technology.


Copyright: – Tom Coughlin – “Machine Learning Creates New Technologies”


This week I was at the 2023 IEEE Intermag Conference in Sendai, Japan. This is a conference put on by the IEEE Magnetics Society (my first IEEE Society, member for 45 years). I was invited to attend as the President Elect of the IEEE. There were over 1,700 total physical and virtual attendees with close to 1,500 people at the conference in person. I believe that this is the largest magnetic conference since the Covid pandemic began in 2020.

I attended a session that had papers on applications of artificial intelligence for magnetic materials research. This is an example of discussions going on in the scientific and engineering community on how people can effectively use new AI tools to accelerate and assist in our understanding of the physical world and its applications to real world applications. These include making better magnetic memory devices, more efficient motors and many other practical activities.

This session included Mingda Li, from MIT who said that “data-fitting is one among many other uses that can be benefited from machine learning. The other is the focus on exploring hidden data, or building structure-property relations.” For this latter application, the papers in this session utilized large material data bases. Mingda mentions a 146,000 materials database in this paper.

Y. Iwasaki from the National Institute for Materials Science, Tsukuba, Ibaraki, Japan used an autonomous materials search system combining machine learning and ab initio calculation to find multi-elemental compositions that could find alloy magnetizations higher than Fe3Co (the material at the peak of the Slater-Pauling curve). The image below, shows the results of this materials search over a 9-week period, gradually finding ways to increase the intrinsic magnetization of the modeled alloy.

This research indicated that adding a bit of Ir and a bit of Pt could increase the magnetization of an iron cobalt alloy. When some physical iron cobalt iridium and iron cobalt platinum allows were made and measured it was found that about 4% Ir did indeed increase the magnetization of the FeCo alloy. Likewise, a little Pt in an FeCo alloy also increased the magnetization. Although alloy compositions with magnetization higher than Fe3Co have been found before, this investigation showed an example of how AI could be used as a tool for new material discoveries.[…]

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