The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Machine learning: Accelerating materials development for energy storage and conversion
Open accessAbstract
With the development of modern society, the requirement for energy has become increasingly important on a global scale. Therefore, the exploration of novel materials for renewable energy technologies is urgently needed. Traditional methods are difficult to meet the requirements for materials science due to long experimental period and high cost. Nowadays, machine learning (ML) is rising as a new research paradigm to revolutionize materials discovery. In this review, we briefly introduce the basic procedure of ML and common algorithms in materials science, and particularly focus on latest progress in applying ML to property prediction and materials development for energy‐related fields, including catalysis, batteries, solar cells, and gas capture. Moreover, contributions of ML to experiments are involved as well. We highly expect that this review could lead the way forward in the future development of ML in materials science. image
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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
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
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., & Tenenbaum, J. B. (2015) · Science · doi:10.1126/science.aab3050 · Cited by 2,817 · cited 1×
“The key is learning to learn, also called meta learning, which has been used in image recognition.(208,209) Several approaches(210,211) were developed for meta learning and it is worth introducing to materials science for limited datasets.”
MethodOn representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312
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
Mounet, N., Gibertini, M., Schwaller, P., Campi, D., Merkys, A., Marrazzo, A., Sohier, T., Castelli, I. E., Cepellotti, A., Pizzi, G., et al. (2018) · Nature Nanotechnology · doi:10.1038/s41565-017-0035-5 · Cited by 1,863
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Duan, C., Chen, S., Taylor, M. G., Liu, F., & Kulik, H. J. (2021) · Chemical Science · doi:10.1039/d1sc03701c · Cited by 40
Zhao, R., Xing, B., Mu, H., Fu, Y., & Zhang, L. (2022) · Chinese Physics B · doi:10.1088/1674-1056/ac5d2d · Cited by 21
Active learning of neural network potentials for rare events
(2024) · doi:10.1039/d3dd00216k · Cited by 20
Knowledge-guided large language model for material science
Wang, G., Hu, J., Zhou, J., Liu, S., Li, Q., & Sun, Z. (2025) · Review of Materials Research · doi:10.1016/j.revmat.2025.100007 · Cited by 16
Velpandian, M., & Basu, S. (2025) · Indian Chemical Engineer · doi:10.1080/00194506.2024.2444394 · Cited by 9
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Metadata
- Authors
- Chen, A., Zhang, X., & Zhou, Z.
- Venue
- InfoMat
- Publisher
- Wiley
- Published
- 2020-02-23
- Volume
- 2
- Issue
- 3
- Pages
- 553-576
- ISSN
- 2567-3165 (ISSN-L)
- OpenAlex
- W3007550778
- Cited by
- 370
- References
- 28
- FWCI
- 17.81
- Type
- journal article
- Language
- en
- Open access
- gold
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