The DOI matches its Crossref record; 101 of the 101 references it cites were found in a registry or an index.
Deep materials informatics: Applications of deep learning in materials science
Open accessAbstract
The growing application of data-driven analytics in materials science has led to the rise of materials informatics. Within the arena of data analytics, deep learning has emerged as a game-changing technique in the last few years, enabling numerous real-world applications, such as self-driving cars. In this paper, the authors present an overview of deep learning, its advantages, challenges, and recent applications on different types of materials data. The increasingly availability of materials databases and big data in general, along with groundbreaking advances in deep learning offers a lot of promise to accelerate the discovery, design, and deployment of next-generation materials.
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Cites
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Cited by
Recent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
“Such an outburst of data requires automated analysis which can be facilitated by machine learning (ML) techniques 13 – 20 .”
MethodSelf-citationEmerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446
Jha, D., Choudhary, K., Tavazza, F., Liao, W.-K., Choudhary, A., Campbell, C., & Agrawal, A. (2019) · Nature Communications · doi:10.1038/s41467-019-13297-w · Cited by 339
Self-citationGraph convolutional neural networks with global attention for improved materials property prediction
Louis, S.-Y., Zhao, Y., Nasiri, A., Wang, X., Song, Y., Liu, F., & Hu, J. (2020) · Physical Chemistry Chemical Physics · doi:10.1039/d0cp01474e · Cited by 257
Metadata
- Authors
- Agrawal, A., & Choudhary, A.
- Venue
- MRS Communications
- Published
- 2019-09
- Volume
- 9
- Issue
- 3
- Pages
- 779-792
- ISSN
- 2159-6859 (ISSN-L)
- 2159-6867
- OpenAlex
- W2953053221
- Cited by
- 294
- References
- 101
- FWCI
- 8.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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