The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Accelerating the discovery of materials for clean energy in the era of smart automation
Open accessAbstract
The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry, materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery, which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace. The discovery and development of advanced materials are imperative for the clean energy sector. We envision that a closed-loop approach, which combines high-throughput computation, artificial intelligence and advanced robotics, will sizeably reduce the time to deployment and the costs associated with materials development.
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Cites
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Cited by
Chatenet, M., Pollet, B. G., Dekel, D. R., Dionigi, F., Deseure, J., Millet, P., Braatz, R. D., Bazant, M. Z., Eikerling, M., Staffell, I., et al. (2022) · Chemical Society Reviews · doi:10.1039/d0cs01079k · Cited by 2,025
Scaling deep learning for materials discovery
Merchant, A., Batzner, S., Schoenholz, S. S., Aykol, M., Cheon, G., & Cubuk, E. D. (2023) · Nature · doi:10.1038/s41586-023-06735-9 · Cited by 1,527
Self-citationBurger, B., Maffettone, P. M., Gusev, V. V., Aitchison, C. M., Bai, Y., Wang, X., Li, X., Alston, B. M., Li, B., Clowes, R., et al. (2020) · Nature · doi:10.1038/s41586-020-2442-2 · Cited by 1,516
Closed-loop optimization of fast-charging protocols for batteries with machine learning
Attia, P. M., Grover, A., Jin, N., Severson, K. A., Markov, T. M., Liao, Y.-H., Chen, M. H., Cheong, B., Perkins, N., Yang, Z., et al. (2020) · Nature · doi:10.1038/s41586-020-1994-5 · Cited by 1,180
Self-citationMuratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913
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Metadata
- Authors
- Tabor, D. P., Roch, L. M., Saikin, S. K., Kreisbeck, C., Sheberla, D., Montoya, J. H., Dwaraknath, S., Aykol, M., Ortiz, C., Tribukait, H., Amador-Bedolla, C., Brabec, C. J., Maruyama, B., Persson, K. A., & Aspuru-Guzik, A.
- Venue
- Nature Reviews Materials
- Published
- 2018-04-26
- Volume
- 3
- Issue
- 5
- Pages
- 5-20
- ISSN
- 2058-8437 (ISSN-L)
- OpenAlex
- W2799915630
- Cited by
- 776
- References
- 31
- FWCI
- 2.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Tabor, D. P., Roch, L. M., Saikin, S. K., Kreisbeck, C., Sheberla, D., Montoya, J. H., Dwaraknath, S., Aykol, M., Ortiz, C., Tribukait, H., Amador-Bedolla, C., Brabec, C. J., Maruyama, B., Persson, K. A., & Aspuru-Guzik, A. (2018). Accelerating the discovery of materials for clean energy in the era of smart automation. Nature Reviews Materials. https://doi.org/10.1038/s41578-018-0005-z
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
