Machine learning-assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials.

Clicks: 284
ID: 91369
2019
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Abstract
In the process of finding high-performance materials for organic photovoltaics (OPVs), it is meaningful if one can establish the relationship between chemical structures and photovoltaic properties even before synthesizing them. Here, we first establish a database containing over 1700 donor materials reported in the literature. Through supervised learning, our machine learning (ML) models can build up the structure-property relationship and, thus, implement fast screening of OPV materials. We explore several expressions for molecule structures, i.e., images, ASCII strings, descriptors, and fingerprints, as inputs for various ML algorithms. It is found that fingerprints with length over 1000 bits can obtain high prediction accuracy. The reliability of our approach is further verified by screening 10 newly designed donor materials. Good consistency between model predictions and experimental outcomes is obtained. The result indicates that ML is a powerful tool to prescreen new OPV materials, thus accelerating the development of the OPV field.
Reference Key
sun2019machinescience Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sun, Wenbo;Zheng, Yujie;Yang, Ke;Zhang, Qi;Shah, Akeel A;Wu, Zhou;Sun, Yuyang;Feng, Liang;Chen, Dongyang;Xiao, Zeyun;Lu, Shirong;Li, Yong;Sun, Kuan;
Journal Science advances
Year 2019
DOI
10.1126/sciadv.aay4275
URL
Keywords
construction industry fuzzy dematel building hse performance hse

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