Material Discovery
Author:
Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-44622-1_11
Reference18 articles.
1. A.M. Gopakumar, P.V. Balachandran, D. Xue, J.E. Gubernatis, T. Lookman, Multi-objective optimization for materials discovery via adaptive design. Sci. Rep. 8(1), 3738 (2018). ISSN: 2045-2322. https://doi.org/10.1038/s41598-018-21936-3.[Online]. https://www.nature.com/articles/s41598-018-21936-3. Accessed 14 Feb 2019
2. T.C. Le, D.A. Winkler, Discovery and optimization of materials using evolutionary approaches. Chem. Rev. 116(10), 6107–6132 (2016). PMID: 27171499. https://doi.org/10.1021/acs.chemrev.5b00691 [Online]
3. K. Hatakeyama-Sato, K. Oyaizu, Generative models for extrapolation prediction in materials informatics. ACS Omega (2021)
4. B. Sanchez-Lengeling, A. Aspuru-Guzik, Inverse molecular design using machine learning: generative models for matter engineering. Science 361(6400), 360–365 (2018). ISSN: 0036-8075. https://doi.org/10.1126/science.aat2663.https://science.sciencemag.org/content/361/6400/360. [Online]
5. Y. Dan, Y. Zhao, X. Li, S. Li, M. Hu, J. Hu, Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials. Npj Comput. Mater. 6(1), 84 (2020). ISSN: 2057-3960. https://doi.org/10.1038/s41524-020-00352-0. [Online]
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