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Microstructure representation learning using Siamese networks

Published online by Cambridge University Press:  18 September 2020

Avadhut Sardeshmukh Open the ORCID record for Avadhut Sardeshmukh [Opens in a new window] ,
Sreedhar Reddy ,
B.P. Gautham  and
Pushpak Bhattacharyya
Avadhut Sardeshmukh*
Affiliation:
TRDDC, TCS Research, Tata Consultancy Services, Pune, India
Sreedhar Reddy
Affiliation:
TRDDC, TCS Research, Tata Consultancy Services, Pune, India
B.P. Gautham
Affiliation:
TRDDC, TCS Research, Tata Consultancy Services, Pune, India
Pushpak Bhattacharyya
Affiliation:
Department of Computer Science and Engineering, IIT Bombay, Mumbai, India
*
Address all correspondence to Avadhut Sardeshmukh at avadhut.sardeshmukh@tcs.com
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Abstract

Obtaining a good statistical representation of material microstructures is crucial for establishing robust process–structure–property linkages and machine learning techniques can bridge this gap. One major difficulty in leveraging recent advances in deep learning for this purpose is the scarcity of good quality data with enough metadata. In machine learning, similarity metric learning using Siamese networks has been used to deal with sparse data. Inspired by this, the authors propose a Siamese architecture to learn microstructure representations. The authors show that analysis tasks such as the classification of microstructures can be done more efficiently in the learned representation space.

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Type
Research Letters
Information
MRS Communications , Volume 10 , Issue 4 , December 2020 , pp. 613 - 619
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Copyright
Copyright © The Author(s), 2020, published on behalf of Materials Research Society by Cambridge University Press

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References

National Research Council: Integrated Computational Materials Engineering: A Transformational Discipline for Improved Competitiveness and National Security (The National Academies Press, Washington, DC, 2008).Google Scholar
Kalidindi, S.R.: 1 - Materials, data, and informatics. In Hierarchical Materials Informatics (Butterworth-Heinemann, Boston, 2015) pp. 1–32.Google Scholar
Bostanabad, R., Zhang, Y., Li, X., Kearney, T., Brinson, L.C., Apley, D., Liu, W.K., and Chen, W.: Computational microstructure characterization and reconstruction: review of the state-of-the-art techniques. Prog. Mater. Sci. 95, 1–41 (2018).CrossRefGoogle Scholar
Cang, R., Li, H., Yao, H., Jiao, Y., and Ren, Y.: Improving direct physical properties prediction of heterogeneous materials from imaging data via convolutional neural network and a morphology-aware generative model. Comput. Mater. Sci. 150, 212–221 (2018).CrossRefGoogle Scholar
LeSar, R.: Materials informatics: an emerging technology for materials development. Stat. Anal. Data Min. 1, 372–374 (2009).CrossRefGoogle Scholar
Rajan, K.: Materials informatics. Mater. Today 15, 470–471 (2012).CrossRefGoogle Scholar
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., and Kim, C.: Machine learning in materials informatics: recent applications and prospects. npj Comput. Mater. 3, Article number 54 (2017).CrossRefGoogle Scholar
Dimiduk, D.M., Holm, E.A., and Niezgoda, S.: Perspectives on the impact of machine learning, deep learning, and artificial intelligence on materials, processes, and structures engineering. Integr. Mater. Manuf. Innov. 8, 1–16 (2018).Google Scholar
Kondo, R., Yamakawa, S., Masuoka, Y., Tajima, S., and Asahi, R.: Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics. Acta Mater. 141, 29–38 (2017).CrossRefGoogle Scholar
Simonyan, K. and Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR, vol. abs/1409.1556 (2014).Google Scholar
Lubbers, N., Lookman, T., and Barros, K.: Inferring low-dimensional microstructure representations using convolutional neural networks. Phys. Rev. E 96, 052111 (2017).CrossRefGoogle ScholarPubMed
Gatys, L., Ecker, A.S., and Bethge, M.: Texture synthesis using convolutional neural networks. In Advances in Neural Information Processing Systems, Vol. 28, edited by Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., and Garnett, R. (Curran Associates, Inc., NY, USA, 2015) pp. 262–270.Google Scholar
DeCost, B.L., Francis, T., and Holm, E.A.: Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures. Acta Mater. 133, 30–40 (2017).CrossRefGoogle Scholar
Arandjelovic, R. and Zisserman, A.: All about VLAD. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, Washington; 2013.CrossRefGoogle Scholar
Chopra, S., Hadsell, R., and LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1, IEEE Computer Society, Washington; 2005.Google Scholar
Hadsell, R., Chopra, S., and LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2, IEEE Computer Society, Washington; 2006.Google Scholar
Koch, G., Zemel, R., and Salakhutdinov, R.: Siamese neural networks for one-shot image recognition. In Proceedings of the 32nd International Conference on Machine Learning, JMLR.org, Lille; 2015.Google Scholar
Bromleyn, J., Guyon, I., LeCun, Y., Säckinger, E., and Shah, R.: Signature verification using a “siamese” time delay neural network. In Advances in Neural Information Processing Systems 6, edited by J.D. Cowan, G. Tesauro and J. Alspector (Morgan Kaufmann Publishers Inc, San Francisco, CA, USA, 1994) pp. 737–744.CrossRefGoogle Scholar
DeCost, B.L., Hecht, M.D., Francis, T., Webler, B.A., Picard, Y.N., and Holm, E.A.: UHCSDB: ultrahigh carbon steel micrograph database. Integr. Mater. Manuf. Innov. 6, 197–205 (2017).CrossRefGoogle Scholar
Hecht, MD, Decost, BL, Francis, T, Picard, YN, Holm, EA and Webler, BA: Ultra High Carbon Steel Micrographs. https://hdl.handle.net/11256/940.Google Scholar
Hecht, M.D., DeCost, B.L., Francis, T., Picard, Y.N., Holm, E.A., and Webler, B.A.: Ultra High Carbon Steel Micrographs. https://hdl.handle.net/11256/940, 2017.Google Scholar
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