Data-enhanced deep greedy optimization algorithm for the on-demand inverse design of tmdc-cavity heterojunctions

HIGHLIGHTS

  • who: Zeyu Zhao and colleagues from the State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha, China have published the research work: Data-Enhanced Deep Greedy Optimization Algorithm for the On-Demand Inverse Design of TMDC-Cavity Heterojunctions, in the Journal: Nanomaterials 2022, 12, x FOR PEER REVIEW in the refractive of /2022/
  • what: The aim of this work is to achieve the on-demand design of cavity-TMDCbased heterojunctions utilizing relatively few reflection spectra generated by traditional RCWA simulations. Five different categories of cavity-TMDC-based heterojunctions . . .

     

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