Computational optimization of MASnI 3 perovskite solar cells using SCAPS-1D simulations and machine learning techniques

RSC Advances 2026 Open access

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Authors: Benjer Islam, Tanvir Mahtab Khan, Md. Mountasir Rahaman, Sheikh Rashel Al Ahmed
DOI: 10.1039/d5ra07200j
Cited by: 23
References: 101

Abstract

value of 0.9999, along with a low mean squared error (MSE) of 0.0092 and mean absolute error (MAE) of 0.051. In addition, the individual influence of several input parameters on the device efficiency has been assessed using the feature importance method. Among the evaluated features, defect density emerged as the most influential parameter, indicating that it plays a significant role in defining the overall performance of the photovoltaic (PV) device.

Fields of Study & Subjects

Perovskite Materials and Applications Machine Learning in Materials Science Heusler alloys: electronic and magnetic properties

Publication details

Journal / source
RSC Advances
Publisher
Royal Society of Chemistry (RSC)
Volume
16
Issue
2
Pages
1172–1192
Publication date
1 January 2026
Language
EN