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Generative Deep Neural Networks for Inverse Materials Design Using Backpropagation and Active Learning
In recent years, machine learning (ML) techniques are seen to be promising tools to discover and design novel materials. However, the lack of robust inverse design approaches to identify promising candidate materials without exploring the entire design space causes a fundamental bottleneck. A genera...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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John Wiley and Sons Inc.
2020
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7055566/ https://www.ncbi.nlm.nih.gov/pubmed/32154072 http://dx.doi.org/10.1002/advs.201902607 |