The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
The 2019 materials by design roadmap
Open accessAbstract
Advances in renewable and sustainable energy technologies critically depend on our ability to design and realize materials with optimal properties. Materials discovery and design efforts ideally involve close coupling between materials prediction, synthesis and characterization. The increased use of computational tools, the generation of materials databases, and advances in experimental methods have substantially accelerated these activities. It is therefore an opportune time to consider future prospects for materials by design approaches. The purpose of this Roadmap is to present an overview of the current state of computational materials prediction, synthesis and characterization approaches, materials design needs for various technologies, and future challenges and opportunities that must be addressed. The various perspectives cover topics on computational techniques, validation, materials databases, materials informatics, high-throughput combinatorial methods, advanced characterization approaches, and materials design issues in thermoelectrics, photovoltaics, solid state lighting, catalysts, batteries, metal alloys, complex oxides and transparent conducting materials. It is our hope that this Roadmap will guide researchers and funding agencies in identifying new prospects for materials design.
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Advanced capabilities for materials modelling with Q uantum ESPRESSO
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Self-citationMachine-learning-assisted materials discovery using failed experiments
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Self-citationComparing molecules and solids across structural and alchemical space
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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
Data‐Driven Materials Science: Status, Challenges, and Perspectives
Himanen, L., Geurts, A., Foster, A. S., & Rinke, P. (2019) · Advanced Science · doi:10.1002/advs.201900808 · Cited by 808
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
Symbolic regression in materials science
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Opportunities and challenges of text mining in materials research
Kononova, O., He, T., Huo, H., Trewartha, A., Olivetti, E. A., & Ceder, G. (2021) · iScience · doi:10.1016/j.isci.2021.102155 · Cited by 182
Metadata
- Authors
- Alberi, K., Nardelli, M. B., Zakutayev, A., Mitas, L., Curtarolo, S., Jain, A., Fornari, M., Marzari, N., Takeuchi, I., Green, M. L., Kanatzidis, M., Toney, M. F., Butenko, S., Meredig, B., Lany, S., Kattner, U., Davydov, A., Toberer, E. S., Stevanovic, V., Walsh, A., Park, N.-G., Aspuru-Guzik, A., Tabor, D. P., Nelson, J., Murphy, J., Setlur, A., Gregoire, J., Li, H., Xiao, R., Ludwig, A., Martin, L. W., Rappe, A. M., Wei, S.-H., & Perkins, J.
- Venue
- Journal of Physics D: Applied Physics
- Publisher
- IOP Publishing
- Published
- 2018-10-24
- Volume
- 52
- Issue
- 1
- Pages
- 013001
- ISSN
- 0022-3727 (ISSN-L)
- 1361-6463
- OpenAlex
- W2886177874
- Cited by
- 322
- References
- 9
- FWCI
- 15.45
- Type
- journal article
- Language
- en
- Open access
- hybrid
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