Progress on material characterization methods under big data environment-Reference-Cited by-同舟云学术

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Progress on material characterization methods under big data environment

Published:2021-03-16 Issue:2 Volume:4 Page:235-247
ISSN:2522-0128
Container-title:Advanced Composites and Hybrid Materials
language:en
Short-container-title:Adv Compos Hybrid Mater

Author:

Chen Lihao,Lan Chuwen,Xu Ben,Bi Ke^ORCID

Funder

National Natural Science Foundation of China

NSAF Joint Fund

Beijing Youth Top-Notch Talent Support Program, Science and Technology Plan of Shenzhen City

Key area research plan of Guangdong

Publisher

Springer Science and Business Media LLC

Subject

Materials Chemistry,Polymers and Plastics,Materials Science (miscellaneous),Ceramics and Composites

Link

https://link.springer.com/content/pdf/10.1007/s42114-021-00229-w.pdf

Reference83 articles.

1. Bock F, Aydin R, Cyron C, Huber N, Kalidindi S, Klusemann B (2019) A review of the application of machine learning and data mining approaches in continuum materials mechanics. Front Mater 6:110

2. Wu L, Xiao Y, Ghosh M, Zhou Q, Hao Q (2020) Machine learning prediction for bandgaps of inorganic materials. ES Materials & Manufacturing

3. Takahashi K, Tanaka Y (2016) Materials informatics: a journey towards material design and synthesis. Dalton Trans 45:10497–10499

4. Kirchdoerfer T, Ortiz M (2017) Data driven computing with noisy material data sets. Comput Methods Appl Mech Eng 326:622–641

5. Kalinin SV, Sumpter BG, Archibald RK (2015) Big-deep-smart data in imaging for guiding materials design. Nat Mater 14:973–980

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