Genetic Algorithm-Based Hybrid Deep Learning Framework for Stability Prediction of ABO 3 Perovskites in Solar Cell Applications

Samad Wali, Muhammad Irfan Khan, Miao Zhang () and Abdul Shakoor
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Samad Wali: General Education Centre, Quanzhou University of Information Engineering, Quanzhou 362000, China
Muhammad Irfan Khan: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Miao Zhang: School of Software, Quanzhou University of Information Engineering, Quanzhou 362000, China
Abdul Shakoor: Department of Mathematics, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

Energies, 2025, vol. 18, issue 19, 1-22

Abstract: The intrinsic structural stability of ABO 3 perovskite materials is a pivotal factor determining their efficiency and durability in photovoltaic applications. However, accurately predicting stability, commonly measured by the energy above hull metric, remains challenging due to the complex interplay of compositional, crystallographic, and electronic features. To address this challenge, we propose a streamlined hybrid machine learning framework that combines the sequence modeling capability of Long Short-Term Memory (LSTM) networks with the robustness of Random Forest regressors. A genetic algorithm-based feature selection strategy is incorporated to identify the most relevant descriptors and reduce noise, thereby enhancing both predictive accuracy and interpretability. Comprehensive evaluations on a curated ABO 3 dataset demonstrate strong performance, achieving an R 2 of 0.98 on training data and 0.83 on independent test data, with a Mean Absolute Error (MAE) of 8.78 for training and 21.23 for testing, and Root Mean Squared Error (RMSE) values that further confirm predictive reliability. These results validate the effectiveness of the proposed approach in capturing the multifactorial nature of perovskite stability while ensuring robust generalization. This study highlights a practical and reliable pathway for accelerating the discovery and optimization of stable perovskite materials, contributing to the development of more durable next-generation solar technologies.

Keywords: perovskite solar cells; ABO 3 perovskites; stability prediction; genetic algorithm; hybrid deep learning; LSTM; Random Forest; feature selection; materials informatics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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