Data from: Machine learning assisted designing of organic solar cell hole-transport molecules with promising short circuit current density

Organic solar cells (OSCs) have shown tremendous potential as a renewable
energy source, but their efficiency is largely dependent on the design of
the hole-transport layer. In this study, we employed machine learning (ML)
techniques to design and optimize organic donors for OSCs. A dataset of
940 small molecule donors (SMDs) was curated from peer-reviewed research
papers, along with their experimental short-circuit voltage (Jsc) values.
Using gradient boost and AdaBoost regressors, we achieved a high
prediction accuracy for Jsc with an R-Squared (R2) value of over 0.90. Our
feature importance analysis revealed that MinAbsEStateIndex and
fr_thiazole have a significant impact on the model. Leveraging the trained
model, we designed 1726 new SMDs with a high structure-activity landscape
index (SALI) score of up to 9.6, indicating their potential as efficient
hole-transport materials. Further, t-SNE and K-Means clustering analysis
was performed to identify patterns and clusters in the designed SMDs. This
work demonstrates the power of ML in reducing computational and
experimental costs associated with the design and optimization of SMDs for
OSCs. By streamlining the design process, our approach can accelerate the
development of more efficient OSCs, ultimately contributing to the
advancement of renewable energy technologies.