ArticleMolecules (Basel, Switzerland)2026
Formation Energy Prediction and Feature Analysis of Lead-Free Double Perovskites Based on Deep Learning.
Article in Molecules (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Lead-free double perovskites (LFDPs) are emerging as promising low-toxicity and thermally stable candidates to replace lead-based perovskites in photovoltaic and optoelectronic devices. However, the rational design of high-stability LFDPs is severely constrained by the low efficiency of conventional experiments and density functional theory (DFT) calculations, as well as the limited accuracy and interpretability of existing machine learning models. To address these limitations, this study employed classic deep learning models to predict the DFT-calculated formation energy of the A
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