Designing mechanically tough graphene oxide materials using deep reinforcement learning
Abstract Graphene oxide (GO) is playing an increasing role in many technologies. However, it remains unanswered how to strategically distribute the functional groups to further enhance performance. We utilize deep reinforcement learning (RL) to design mechanically tough GOs. The design task is formu...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-11-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-022-00919-z |
| _version_ | 1797984718204960768 |
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| author | Bowen Zheng Zeyu Zheng Grace X. Gu |
| author_facet | Bowen Zheng Zeyu Zheng Grace X. Gu |
| author_sort | Bowen Zheng |
| collection | DOAJ |
| description | Abstract Graphene oxide (GO) is playing an increasing role in many technologies. However, it remains unanswered how to strategically distribute the functional groups to further enhance performance. We utilize deep reinforcement learning (RL) to design mechanically tough GOs. The design task is formulated as a sequential decision process, and policy-gradient RL models are employed to maximize the toughness of GO. Results show that our approach can stably generate functional group distributions with a toughness value over two standard deviations above the mean of random GOs. In addition, our RL approach reaches optimized functional group distributions within only 5000 rollouts, while the simplest design task has 2 × 1011 possibilities. Finally, we show that our approach is scalable in terms of the functional group density and the GO size. The present research showcases the impact of functional group distribution on GO properties, and illustrates the effectiveness and data efficiency of the deep RL approach. |
| first_indexed | 2024-04-11T07:06:10Z |
| format | Article |
| id | doaj.art-df450f1c74e2443886d2940ec4847764 |
| institution | Directory Open Access Journal |
| issn | 2057-3960 |
| language | English |
| last_indexed | 2024-04-11T07:06:10Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj.art-df450f1c74e2443886d2940ec48477642022-12-22T04:38:23ZengNature Portfolionpj Computational Materials2057-39602022-11-01811910.1038/s41524-022-00919-zDesigning mechanically tough graphene oxide materials using deep reinforcement learningBowen Zheng0Zeyu Zheng1Grace X. Gu2Department of Mechanical Engineering, University of CaliforniaDepartment of Industrial Engineering and Operations Research, University of CaliforniaDepartment of Mechanical Engineering, University of CaliforniaAbstract Graphene oxide (GO) is playing an increasing role in many technologies. However, it remains unanswered how to strategically distribute the functional groups to further enhance performance. We utilize deep reinforcement learning (RL) to design mechanically tough GOs. The design task is formulated as a sequential decision process, and policy-gradient RL models are employed to maximize the toughness of GO. Results show that our approach can stably generate functional group distributions with a toughness value over two standard deviations above the mean of random GOs. In addition, our RL approach reaches optimized functional group distributions within only 5000 rollouts, while the simplest design task has 2 × 1011 possibilities. Finally, we show that our approach is scalable in terms of the functional group density and the GO size. The present research showcases the impact of functional group distribution on GO properties, and illustrates the effectiveness and data efficiency of the deep RL approach.https://doi.org/10.1038/s41524-022-00919-z |
| spellingShingle | Bowen Zheng Zeyu Zheng Grace X. Gu Designing mechanically tough graphene oxide materials using deep reinforcement learning npj Computational Materials |
| title | Designing mechanically tough graphene oxide materials using deep reinforcement learning |
| title_full | Designing mechanically tough graphene oxide materials using deep reinforcement learning |
| title_fullStr | Designing mechanically tough graphene oxide materials using deep reinforcement learning |
| title_full_unstemmed | Designing mechanically tough graphene oxide materials using deep reinforcement learning |
| title_short | Designing mechanically tough graphene oxide materials using deep reinforcement learning |
| title_sort | designing mechanically tough graphene oxide materials using deep reinforcement learning |
| url | https://doi.org/10.1038/s41524-022-00919-z |
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