Predicting power conversion efficiency of organic photovoltaics: models and data analysis

• المؤلفون الرئيسيون: Eibeck, Andreas, Nurkowski, Daniel, Menon, Angiras, Bai, Jiaru, Wu, Jinkui, Zhou, Li, Mosbach, Sebastian, Akroyd, Jethro, Kraft, Markus
• مؤلفون آخرون: School of Chemical and Biomedical Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Chemical engineering; Solar-Cells; Clean Energy Project
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/160582

In this paper, the ability of three selected machine learning neural and baseline models in predicting the power conversion efficiency (PCE) of organic photovoltaics (OPVs) using molecular structure information as an input is assessed. The bidirectional long short-term memory (gFSI/BiLSTM), attentiv...