Application of Deep Learning in Materials Design: Extraction of Process-Structure-Property Relationship
Author:
Affiliation:
1. Research Institute for Value-Added-Information Generation, Japan Agency for Marine-Earth Science and Technology
2. Institute for Industrial Science, The University of Tokyo
Publisher
Japanese Society of Applied Statistics
Link
https://www.jstage.jst.go.jp/article/jappstat/52/2/52_75/_pdf
Reference128 articles.
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2. Allison, John (2011) ‘Integrated computational materials engineering: A perspective on progress and future steps.’ JOM 63(4), 15.
3. Allison, John, Dan Backman, and Leo Christodoulou (2006) ‘Integrated computational materials engineering: a new paradigm for the global materials profession.’ JOM 58(11), 25-27.
4. Azimi, Seyed Majid, Dominik Britz, Michael Engstler, Mario Fritz, and Frank Mücklich (2018) ‘Advanced steel microstructural classification by deep learning methods.’ Sci. Rep. 8(1), 1-14.
5. Banko, Lars, Yury Lysogorskiy, Dario Grochla, Dennis Naujoks, Ralf Drautz, and Alfred Ludwig (2020) ‘Predicting structure zone diagrams for thin film synthesis by generative machine learning.’ Commun. Mater. 1(1), 1-10.
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