High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks
| Autor: | Yuqi Song, Ming Hu, Edirisuriya M. D. Siriwardane, Jianjun Hu, Yong Zhao, Mohammed Al-Fahdi, Alireza Nasiri |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
cubic crystals
Computer science General Chemical Engineering Distributed computing Science General Physics and Astronomy Medicine (miscellaneous) FOS: Physical sciences Cubic crystal system Biochemistry Genetics and Molecular Biology (miscellaneous) General Materials Science crystal structure generation Generative Design Throughput (business) Research Articles Structure (mathematical logic) Condensed Matter - Materials Science Artificial neural network business.industry Deep learning Scale (chemistry) General Engineering Materials Science (cond-mat.mtrl-sci) deep neural networks Artificial intelligence generative adversarial networks business Generative grammar Research Article |
| Zdroj: | Advanced Science Advanced Science, Vol 8, Iss 20, Pp n/a-n/a (2021) |
| ISSN: | 2198-3844 |
| Popis: | High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of quantity and diversity of known materials deposited in the current materials repositories such as ICSD and OQMD. Recent progress in machine learning and especially deep learning have enabled a generative strategy that learns implicit chemical rules for creating chemically valid hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generation of novel cubic crystal structures. When trained on 375,749 ternary crystal materials from the OQMD database, we show that our model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such new materials (all of them are either ternary or quarternary) have been verified by DFT based phonon dispersion stability check, several of which have been found to potentially have exceptional functional properties. Considering the importance of cubic materials in wide applications such as solar cells and lithium batteries, our GAN model provides a promising approach to significantly expand the current repository of materials, enabling the discovery of new functional materials via screening. The new crystal structures finally verified by DFT are freely accessible at our Carolina Materials Database http://www.carolinamatdb.org. Comment: 14 pages, 4 figures |
| Databáze: | OpenAIRE |
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