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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...
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| Veröffentlicht in: | Adv Sci (Weinh) |
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| Hauptverfasser: | , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
John Wiley and Sons Inc.
2020
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7055566/ https://ncbi.nlm.nih.gov/pubmed/32154072 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/advs.201902607 |
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