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Automating material image analysis for material discovery

Published online by Cambridge University Press:  24 April 2019

Chiwoo Park  and
Yu Ding Open the ORCID record for Yu Ding [Opens in a new window]
Chiwoo Park
Affiliation:
Department of Industrial and Manufacturing Engineering, Florida State University, Tallahassee, FL 32310, USA
Yu Ding*
Affiliation:
Department of Industrial and Systems Engineering, Texas A&M University, 3131 TAMU, College Station, TX 77843, USA
*
Address all correspondence to Yu Ding at yuding@tamu.edu
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Abstract

Advancements in temporal and spatial resolutions of microscopes promise to expand the frontiers of understanding in materials science. Imaging techniques produce images at a high-frame rate, streaming out a tremendous amount of data. Analysis of all these images is time-consuming and labor intensive, creating a bottleneck in material discovery that needs to be overcome. This paper summarizes recent progresses in machine learning and data science for expediting and automating material image analysis. The discussion covers both static image and dynamic image analyses, followed by remarks concerning ongoing efforts and future needs in automated image analysis that accelerates material discovery.

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Type
Artificial Intelligence Prospectives
Information
MRS Communications , Volume 9 , Issue 2 , June 2019 , pp. 545 - 555
Copyright
Copyright © Materials Research Society 2019 

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