View in EDS

Data-assisted approach for optimal designing of small molecules for perovskite solar cells.

Saved in:
Bibliographic Details
Title: Data-assisted approach for optimal designing of small molecules for perovskite solar cells.
Authors: Saqib, Muhammad1 (AUTHOR) muhammad.saqib@kfueit.edu.pk, Sagir, Muhammad2 (AUTHOR), Sairah1 (AUTHOR), Tahir, Mudassir Hussain3 (AUTHOR), Elansary, Hosam O.4 (AUTHOR), Javed, Muqadas1 (AUTHOR)
Source: Journal of Solid State Chemistry. May2025, Vol. 345, pN.PAG-N.PAG. 1p.
Subjects: Machine learning, Random forest algorithms, Solar cells, Reorganization energy, Hole mobility, Boosting algorithms
Abstract: Conventional computational methods have long history in designing the organic compounds, however, these approaches generally require significantly higher computational cost. To overcome these challenges, machine learning is applied as a powerful approach to screen and design high performance materials in a rapid and computationally cost-effective manner. Reorganization energy (Re) is predicted using machine learning. Mordred software is used to calculate molecular descriptors. Different algorithms such as random forest regressor, gradient boosting regressor, K-neighbors regressor, and extra tree regressor models are used to train the machine learning models. Random forest regressor model reveals higher predictive capability (R2 = 0.73). Automatic method is used to design new compounds. 30 potential candidates are identified and their synthetic ability score are predicted. Clustering is used for similarity analysis. Interestingly, synthetic accessibility score reveals that these compounds can be synthesize with ease. The proposed approach holds immense potential for screening and designing high performance hole transport materials for perovskite solar cells in a cost-effective and rapid manner. [Display omitted] • Machine learning approach is used for optimal designing of small molecules for perovskite solar cells. • About 04 machine learning regressor models are applied for optimal predictions of targeted properties. • Chemical similarity analysis is used for screening potential candidates for perovskite solar cells. • 30 potential compounds are identified that could be synthesized with ease. • Clustering analysis is used. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Solid State Chemistry is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Conventional computational methods have long history in designing the organic compounds, however, these approaches generally require significantly higher computational cost. To overcome these challenges, machine learning is applied as a powerful approach to screen and design high performance materials in a rapid and computationally cost-effective manner. Reorganization energy (Re) is predicted using machine learning. Mordred software is used to calculate molecular descriptors. Different algorithms such as random forest regressor, gradient boosting regressor, K-neighbors regressor, and extra tree regressor models are used to train the machine learning models. Random forest regressor model reveals higher predictive capability (R2 = 0.73). Automatic method is used to design new compounds. 30 potential candidates are identified and their synthetic ability score are predicted. Clustering is used for similarity analysis. Interestingly, synthetic accessibility score reveals that these compounds can be synthesize with ease. The proposed approach holds immense potential for screening and designing high performance hole transport materials for perovskite solar cells in a cost-effective and rapid manner. [Display omitted] • Machine learning approach is used for optimal designing of small molecules for perovskite solar cells. • About 04 machine learning regressor models are applied for optimal predictions of targeted properties. • Chemical similarity analysis is used for screening potential candidates for perovskite solar cells. • 30 potential compounds are identified that could be synthesized with ease. • Clustering analysis is used. [ABSTRACT FROM AUTHOR]
ISSN:00224596
DOI:10.1016/j.jssc.2025.125250