Facility Agriculture: Application of Solar Facilities in Agriculture【Department of Chemistry / Yeh, Chen-Yu / Distinguished Professor】
設施農業：光能設施於農業固碳之應用【化學系/葉鎮宇特聘教授】
論文篇名 英文： Advanced High-Throughput Rational Design of Porphyrin-Sensitized Solar Cells Using Interpretable Machine Learning
中文：利用可解釋機器學習的先進高通量理性設計紫質敏化太陽能電池
期刊名稱 Advanced Science
發表年份,卷數,起迄頁數 2024, 11(43), 2407235
作者 Liao, Jian-Ming; Chen, Yu-Hsuan; Lee, Hsuan-Wei; Guo, Bo-Cheng; Su, Po-Cheng; Wang, Lun-Hong; Reddy, Nagannagari Masi; Yella, Aswani; Zhang, Zhao-Jie; Chang, Chuan-Yung; Chen, Chia-Yuan; Zakeeruddin, Shaik M.; Tsai, Hui-Hsu Gavin; Yeh, Chen-Yu(葉鎮宇)*; Gratzel, Michael
DOI 10.1002/advs.202407235
中文摘要 準確預測染料敏化太陽能電池（DSSCs）的功率轉換效率（PCE）是一項至關重要的挑戰，特別是對於高通量理性設計和篩選有前景的染料敏化劑。本研究提出了專為鋅紫質敏化太陽能電池設計的精確、可預測和可解釋的機器學習（ML）模型。該模型利用理論可計算的、有效的和可重用的分子描述符（MDs）來解決這一挑戰。模型在17個新設計電池的“盲測”中表現優異，平均絕對誤差（MAE）為1.02%。值得注意的是，10種染料的預測誤差在1%以內。這些結果驗證了ML模型的有效性及其在探索鋅紫質未開發化學空間中的重要性。SHAP分析確定了與實驗觀察良好對應的關鍵MDs，為DSSCs中染料的理性設計提供了寶貴的化學指導。這些預測性ML模型使得高效的計算篩選成為可能，顯著縮短了光伏電池的分析時間。識別出具有卓越PCE的鋅紫質基染料，促進高通量虛擬篩選。預測工具可在https://ai-meta.chem.ncu.edu.tw/dsc-meta。
英文摘要 Accurately predicting the power conversion efficiency (PCE) in dye-sensitized solar cells (DSSCs) represents a crucial challenge, one that is pivotal for the high throughput rational design and screening of promising dye sensitizers. This study presents precise, predictive, and interpretable machine learning (ML) models specifically designed for Zn-porphyrin-sensitized solar cells. The model leverages theoretically computable, effective, and reusable molecular descriptors (MDs) to address this challenge. The models achieve excellent performance on a “blind test” of 17 newly designed cells, with a mean absolute error (MAE) of 1.02%. Notably, 10 dyes are predicted within a 1% error margin. These results validate the ML models and their importance in exploring uncharted chemical spaces of Zn-porphyrins. SHAP analysis identifies crucial MDs that align well with experimental observations, providing valuable chemical guidelines for the rational design of dyes in DSSCs. These predictive ML models enable efficient in silico screening, significantly reducing analysis time for photovoltaic cells. Promising Zn-porphyrin-based dyes with exceptional PCE are identified, facilitating high-throughput virtual screening. The prediction tool is publicly accessible at https://ai-meta.chem.ncu.edu.tw/dsc-meta.
發表成果與本中心研究主題相關性 太陽能電池可將光轉換成電，其中染敏電池中開發新型染料以期提高光電轉換效率的目標在永續農業發展上的重要性不容小覷，因為光源可以是大自然的太陽光，可以是人造的室內弱光，而光則是在農業發展上不可或缺的條件之一。