Work

Designing of small molecule donors with the help of machine learning for organic solar cells and performance prediction

Year: 2024

Type: article

Abstract: Designing of materials for organic solar cells is a tedious process. In present study, machine learning (ML) is used to predict the power conversion efficiency (PCE). Over 40 ML models are tried. Gradient boosting regression is appeared as best model. 10k small molecule donors are designed. Their PCE values are predicted using best model. The library of generated donors is visualized using various tools. Chemical similarity analysis is done to study structural behavior of selected donors. Reasonable resemblance is found. Synthetic accessibility assessment has indicated easy synthesis for majority of selected small molecule donors. The introduced framework has ability to find the efficient materials in short time.

Source: Journal of Photochemistry and Photobiology A Chemistry

DOI: 10.1016/j.jphotochem.2024.116026

Authors: Bilal Siddique, Taghrid S. Alomar, Mudassir Hussain Tahir, Najla AlMasoud, Zeinhom M. El-Bahy

Institutions: University of Okara, University of Education, Princess Nourah bint Abdulrahman University, Hokkaido University, Al-Azhar University

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

SDG: Affordable and clean energy

Open Access status: hybrid

Estimated APC paid: $3,320