AI/ML Perspective in MRS Bulletin

Our new perspective on the use of artificial intelligence (AI) and machine learning in electron and scanning probe microscopy is now out in the MRS Bulletin! I’m grateful to work with other leaders in AI methods to map out recent successes of these techniques in materials science and chemistry. We identify current challenges and emerging opportunities for machine-guided experimentation to accelerate breakthrough discoveries!

From the abstract:

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment in imaging. In this article, we discuss recent progress in application of machine learning methods in scanning transmission electron microscopy and scanning probe microscopy, from applications such as data compression and exploratory data analysis to physics learning to atomic fabrication.

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