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In this study, we developed a computational material screening workflow for metallic electrodes of BaSi 2 solar cells. Elemental and binary metallic materials in the Materials Project database were screened for four device models with different charge transport layer materials contacting the electrode. The screening criteria included chemical stability, melting point, and work function. In contrast to conventional screening approaches that rely solely on materials descriptors, the present workflow explicitly incorporates device-level performance constraints by using the relationship between the work function and the simulated power conversion efficiency. For melting point evaluation, a linear regression estimation from the cohesive energy and a machine learning model were compared to assess their accuracy, which revealed the higher accuracy of the machine learning model. For work function evaluation, first-principles calculation and another machine learning model were compared, which showed similar accuracies. Considering the computational cost, the machine learning model was used for screening. The threshold of work function screening was determined by device simulations. As a result, promising materials for metallic electrodes were successfully identified. Moreover, the developed screening workflow with high versatility will be applicable to material discovery for other solar cells and semiconductor devices.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: Solar cells, metallic electrode, high-throughput virtual screening, work function, melting point, density functional theory

刊行年月日: 2026-12-31

出版者: Taylor & Francis

掲載誌:

• Science and Technology of Advanced Materials (ISSN: 27660400) vol. 6 issue. 1 2617671

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.6208

公開URL: https://doi.org/10.1080/27660400.2026.2617671

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更新時刻: 2026-03-07 12:30:04 +0900

MDRでの公開時刻: 2026-03-07 09:46:04 +0900