The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Machine Learning in Materials Science: Recent Progress and Emerging Applications
Abstract
This chapter addresses the role that data-driven approaches, especially machine learning methods, are expected to play in materials research in the immediate future. Machine learning, an important part of artificial intelligence, has made monumental contributions to areas outside materials science, ranging from commerce to gaming to search engines to drug design. Machine learning algorithms can be separated into two broad classes: supervised and unsupervised learning. The chapter first provides the necessary mathematical background to allow a materials researcher entering this field to use these methods most effectively. It then presents an assortment of examples of recent machine learning applications within materials science. The chapter also discusses a range of emerging efforts, including high-throughput phase diagram and crystal structure determination methods, accelerated prediction of materials properties, development of interatomic potentials and functionals for accelerating materials simulations, and efficient and low-cost methods for materials process control.
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Cites
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Cited by
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945
Self-citationGraph Networks as a Universal Machine Learning Framework for Molecules and Crystals
Chen, C., Ye, W., Zuo, Y., Zheng, C., & Ong, S. P. (2019) · Chemistry of Materials · doi:10.1021/acs.chemmater.9b01294 · Cited by 1,552
Materials discovery and design using machine learning
Liu, Y., Zhao, T., Ju, W., & Shi, S. (2017) · Journal of Materiomics · doi:10.1016/j.jmat.2017.08.002 · Cited by 1,276
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
Ouyang, R., Curtarolo, S., Ahmetcik, E., Scheffler, M., & Ghiringhelli, L. M. (2018) · Physical Review Materials · doi:10.1103/physrevmaterials.2.083802 · Cited by 944
Metadata
- Authors
- Mueller, T., Kusne, A. G., & Ramprasad, R.
- Venue
- Reviews in Computational Chemistry
- Publisher
- Wiley
- Published
- 2016-04
- Pages
- 186-273
- ISSN
- 1069-3599 (ISSN-L)
- 1934-5372
- OpenAlex
- W2503343131
- Cited by
- 295
- References
- 31
- Type
- other
- Language
- en
- Open access
- closed
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- Mueller, T., Kusne, A. G., & Ramprasad, R. (2016). Machine Learning in Materials Science: Recent Progress and Emerging Applications. Reviews in Computational Chemistry. https://doi.org/10.1002/9781119148739.ch4
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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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