Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions
A data-enhanced deep greedy optimization (DEDGO) algorithm is proposed to achieve the efficient and on-demand inverse design of multiple transition metal dichalcogenides (TMDC)-photonic cavity-integrated heterojunctions operating in the strong coupling regime. Precisely, five types of photonic cavit...
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MDPI AG
2022-08-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/12/17/2976 |
| _version_ | 1797493905001807872 |
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| author | Zeyu Zhao Jie You Jun Zhang Yuhua Tang |
| author_facet | Zeyu Zhao Jie You Jun Zhang Yuhua Tang |
| author_sort | Zeyu Zhao |
| collection | DOAJ |
| description | A data-enhanced deep greedy optimization (DEDGO) algorithm is proposed to achieve the efficient and on-demand inverse design of multiple transition metal dichalcogenides (TMDC)-photonic cavity-integrated heterojunctions operating in the strong coupling regime. Precisely, five types of photonic cavities with different geometrical parameters are employed to alter the optical properties of monolayer TMDC, aiming at discovering new and intriguing physics associated with the strong coupling effect. Notably, the traditional rigorous coupled wave analysis (RCWA) approach is utilized to generate a relatively small training dataset for the DEDGO algorithm. Importantly, one remarkable feature of DEDGO is the integration the decision theory of reinforcement learning, which remedies the deficiencies of previous research that focused more on modeling over decision making, increasing the success rate of inverse prediction. Specifically, an iterative optimization strategy, namely, deep greedy optimization, is implemented to improve the performance. In addition, a data enhancement method is also employed in DEDGO to address the dependence on a large amount of training data. The accuracy and effectiveness of the DEDGO algorithm are confirmed to be much higher than those of the random forest algorithm and deep neural network, making possible the replacement of the time-consuming conventional scanning optimization method with the DEDGO algorithm. This research thoroughly describes the universality, interpretability, and excellent performance of the DEDGO algorithm in exploring the underlying physics of TMDC-cavity heterojunctions, laying the foundations for the on-demand inverse design of low-dimensional material-based nano-devices. |
| first_indexed | 2024-03-10T01:26:41Z |
| format | Article |
| id | doaj.art-4dc4db10270d45c3825876956e0e608b |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-10T01:26:41Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-4dc4db10270d45c3825876956e0e608b2023-11-23T13:48:47ZengMDPI AGNanomaterials2079-49912022-08-011217297610.3390/nano12172976Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity HeterojunctionsZeyu Zhao0Jie You1Jun Zhang2Yuhua Tang3State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, ChinaDefense Innovation Institute, Academy of Military Sciences PLA China, Beijing 100071, ChinaState Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, ChinaState Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha 410073, ChinaA data-enhanced deep greedy optimization (DEDGO) algorithm is proposed to achieve the efficient and on-demand inverse design of multiple transition metal dichalcogenides (TMDC)-photonic cavity-integrated heterojunctions operating in the strong coupling regime. Precisely, five types of photonic cavities with different geometrical parameters are employed to alter the optical properties of monolayer TMDC, aiming at discovering new and intriguing physics associated with the strong coupling effect. Notably, the traditional rigorous coupled wave analysis (RCWA) approach is utilized to generate a relatively small training dataset for the DEDGO algorithm. Importantly, one remarkable feature of DEDGO is the integration the decision theory of reinforcement learning, which remedies the deficiencies of previous research that focused more on modeling over decision making, increasing the success rate of inverse prediction. Specifically, an iterative optimization strategy, namely, deep greedy optimization, is implemented to improve the performance. In addition, a data enhancement method is also employed in DEDGO to address the dependence on a large amount of training data. The accuracy and effectiveness of the DEDGO algorithm are confirmed to be much higher than those of the random forest algorithm and deep neural network, making possible the replacement of the time-consuming conventional scanning optimization method with the DEDGO algorithm. This research thoroughly describes the universality, interpretability, and excellent performance of the DEDGO algorithm in exploring the underlying physics of TMDC-cavity heterojunctions, laying the foundations for the on-demand inverse design of low-dimensional material-based nano-devices.https://www.mdpi.com/2079-4991/12/17/2976inverse designtransition metal dichalcogenidesphotonic cavityintegrated heterojunctionstrong coupling effectdeep learning |
| spellingShingle | Zeyu Zhao Jie You Jun Zhang Yuhua Tang Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions Nanomaterials inverse design transition metal dichalcogenides photonic cavity integrated heterojunction strong coupling effect deep learning |
| title | Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions |
| title_full | Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions |
| title_fullStr | Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions |
| title_full_unstemmed | Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions |
| title_short | Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions |
| title_sort | data enhanced deep greedy optimization algorithm for the on demand inverse design of tmdc cavity heterojunctions |
| topic | inverse design transition metal dichalcogenides photonic cavity integrated heterojunction strong coupling effect deep learning |
| url | https://www.mdpi.com/2079-4991/12/17/2976 |
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