Unraveling Correlations between Molecular Properties and Device Parameters of Organic Solar Cells Using Machine Learning

2019 ◽  
Vol 10 (22) ◽  
pp. 7277-7284 ◽  
Author(s):  
Harikrishna Sahu ◽  
Haibo Ma
Keyword(s):  
Machine Learning ◽  
Solar Cells ◽  
Organic Solar Cells ◽  
Molecular Properties

Machine learning and molecular dynamics simulations assisted evolutionary design and discovery pipeline to screen the efficient small molecule acceptors for PTB7-Th based organic solar cells with over 15% efficiency

2022 ◽  
Author(s):  
Jin-Liang Wang ◽  
Asif Mahmood ◽  
Ahmad Irfan
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Solar Cells ◽  
Molecular Dynamics Simulations ◽  
Small Molecule ◽  
Organic Solar Cells ◽  
Evolutionary Design ◽  
New Materials ◽  
Dynamics Simulations ◽  
Discovery Pipeline

Organic solar cells are the most promising candidates for future commercialization. This goal can be quickly achieved by designing new materials and predicting their performance without experimentation to reduce the...

Prediction of non-radiative voltage losses in organic solar cells using machine learning

Solar Energy ◽  
2021 ◽  
Vol 228 ◽  
pp. 175-186
Author(s):  
Prateek Malhotra ◽  
Subhayan Biswas ◽  
Fang-Chung Chen ◽  
Ganesh D. Sharma
Keyword(s):  
Machine Learning ◽  
Solar Cells ◽  
Organic Solar Cells

Combining electronic and structural features in machine learning models to predict organic solar cells properties

2019 ◽  
Vol 6 (2) ◽  
pp. 343-349 ◽  
Author(s):  
Daniele Padula ◽  
Jack D. Simpson ◽  
Alessandro Troisi
Keyword(s):  
Machine Learning ◽  
Solar Cells ◽  
Organic Solar Cells ◽  
Structural Similarity ◽  
Structural Features ◽  
Learning Models ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Machine Learning Models

Combining electronic and structural similarity between organic donors in kernel based machine learning methods allows to predict photovoltaic efficiencies reliably.

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