The DOI matches its Crossref record; 102 of the 102 references it cites were found in a registry or an index.
Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design
Open accessAbstract
One of the main challenges in materials discovery is efficiently exploring the vast search space for targeted properties as approaches that rely on trial-and-error are impractical. We review how methods from the information sciences enable us to accelerate the search and discovery of new materials. In particular, active learning allows us to effectively navigate the search space iteratively to identify promising candidates for guiding experiments and computations. The approach relies on the use of uncertainties and making predictions from a surrogate model together with a utility function that prioritizes the decision making process on unexplored data. We discuss several utility functions and demonstrate their use in materials science applications, impacting both experimental and computational research. We summarize by indicating generalizations to multiple properties and multifidelity data, and identify challenges, future directions and opportunities in the emerging field of materials informatics.
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High-entropy nanoparticles: Synthesis-structure-property relationships and data-driven discovery
Yao, Y., Dong, Q., Brozena, A., Luo, J., Miao, J., Chi, M., Wang, C., Kevrekidis, I. G., Ren, Z. J., Greeley, J., et al. (2022) · Science · doi:10.1126/science.abn3103 · Cited by 1,081
Opportunities and Challenges for Machine Learning in Materials Science
Morgan, D., & Jacobs, R. (2020) · Annual Review of Materials Research · doi:10.1146/annurev-matsci-070218-010015 · Cited by 549
Machine learning assisted materials design and discovery for rechargeable batteries
Liu, Y., Guo, B., Zou, X., Li, Y., & Shi, S. (2020) · Energy Storage Materials · doi:10.1016/j.ensm.2020.06.033 · Cited by 466
Emerging 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
Machine learning in materials science: From explainable predictions to autonomous design
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Metadata
- Authors
- Lookman, T., Balachandran, P. V., Xue, D., & Yuan, R.
- Venue
- npj Computational Materials
- Published
- 2019-02-18
- Volume
- 5
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W2916338083
- Cited by
- 832
- References
- 102
- FWCI
- 21.36
- Type
- journal article
- Language
- en
- Open access
- gold
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