The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
The Role of Machine Learning in the Understanding and Design of Materials
Open accessAbstract
Developing algorithmic approaches for the rational design and discovery of materials can enable us to systematically find novel materials, which can have huge technological and social impact. However, such rational design requires a holistic perspective over the full multistage design process, which involves exploring immense materials spaces, their properties, and process design and engineering as well as a techno-economic assessment. The complexity of exploring all of these options using conventional scientific approaches seems intractable. Instead, novel tools from the field of machine learning can potentially solve some of our challenges on the way to rational materials design. Here we review some of the chief advancements of these methods and their applications in rational materials design, followed by a discussion on some of the main challenges and opportunities we currently face together with our perspective on the future of rational materials design and discovery.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
Jain, A., Ong, S. P., Hautier, G., Chen, W., Richards, W. D., Dacek, S., Cholia, S., Gunter, D., Skinner, D., Ceder, G., et al. (2013) · APL Materials · doi:10.1063/1.4812323 · Cited by 13,094
Rudin, C. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0048-x · Cited by 10,561
The Cambridge Structural Database
Groom, C. R., Bruno, I. J., Lightfoot, M. P., & Ward, S. C. (2016) · Acta Crystallographica Section B Structural Science, Crystal Engineering and Materials · doi:10.1107/s2052520616003954 · Cited by 10,297
Laio, A., & Parrinello, M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202427399 · Cited by 5,626
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010
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Chemical Flexibility of Atomically Precise Metal Clusters
Li, S., Li, N.-N., Dong, X.-Y., Zang, S.-Q., & Mak, T. C. W. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00896 · Cited by 280
Jablonka, K. M., Ai, Q., Al-Feghali, A., Badhwar, S., Bocarsly, J. D., Bran, A. M., Bringuier, S., Brinson, L. C., Choudhary, K., Circi, D., et al. (2023) · Digital Discovery · doi:10.1039/d3dd00113j · Cited by 218
Self-citationA review on machine learning-guided design of energy materials
Kim, S., Xu, J., Shang, W., Xu, Z., Lee, E., & Luo, T. (2024) · Progress in Energy · doi:10.1088/2516-1083/ad7220 · Cited by 19
Data-driven enhanced sampling of mechanistic pathways
Elangovan, R., Chatterjee, S., & Ray, D. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2517169122 · Cited by 3
Metadata
- Authors
- Moosavi, S. M., Jablonka, K. M., & Smit, B.
- Venue
- Journal of the American Chemical Society
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-11-10
- Volume
- 142
- Issue
- 48
- Pages
- 20273-20287
- ISSN
- 0002-7863 (ISSN-L)
- 1520-5126
- OpenAlex
- W3104073206
- Cited by
- 433
- References
- 41
- FWCI
- 16.78
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Moosavi, S. M., Jablonka, K. M., & Smit, B. (2020). The Role of Machine Learning in the Understanding and Design of Materials. Journal of the American Chemical Society. https://doi.org/10.1021/jacs.0c09105
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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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