GAN-augmented machine learning enables accurate band gap prediction for 2D lead iodide perovskites with limited data.

Authors

Zehao Zhang, Jiayi Li, Kunlun Jiang, Wenzhe Li, Jiandong Fan

Published in

Physical chemistry chemical physics : PCCP. Jul 27, 2026. Epub Jul 27, 2026.

Abstract

Low-dimensional hybrid lead iodide perovskites exhibit band gaps that are highly sensitive to subtle octahedral distortions, yet accurate prediction remains challenging under small-data regimes where traditional machine learning models tend to fail. Herein, we develop a collaborative machine-learning framework for two-dimensional (2D) lead iodide perovskites that integrates physically interpretable [PbI₆]^4--based structural descriptors, principal component analysis (PCA) for dimensionality reduction, multi-layer perceptron generative adversarial network (MLP-GAN) data augmentation (generating 1000 synthetic structures), and automated hyperparameter optimization. Using 107 single-crystal experimental data points, we benchmark nine regression models and demonstrate that GAN-based augmentation substantially improves model learning capability and generalization robustness. This study is designed to establish an interpretable data-augmentation strategy for small-data materials modeling and to test its applicability to band-gap prediction in 2D lead iodide perovskites.

PMID:
42504627
Bibliographic data and abstract were imported from PubMed on 27 Jul 2026.

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