MXene-Integrated Perovskite Tandem Solar Cells for Sustainable Development Goals (SDGs): A Bibliometric and Physics-Informed Artificial Intelligence (AI) Analysis Using Graph Neural Networks and Transformer Models
Keywords:
Bibliometric analysis, Graph Neural Networks, MXene, Perovskite tandem solar cells, Sustainable Development GoalsAbstract
This study presents a bibliometric and artificial intelligence
(AI) analysis of MXene-integrated perovskite tandem solar
cells to support renewable energy innovation and the
Sustainable Development Goals (SDGs). A literature-based
bibliometric perspective was used to identify research trends
in perovskite tandem photovoltaics, MXene materials, and
AI-assisted solar cell optimization. A physics-informed
computational framework integrating Graph Neural
Networks (GNNs) and Transformer models was then
developed using 200 photovoltaic samples with 26 material,
environmental, and performance variables. The GNN model
captured nonlinear material–performance relationships,
while the Transformer model predicted retention and
degradation behavior under humidity and thermal stress.
Feature importance analysis identified MXene loading,
conductivity, defect density, and interface quality as
dominant factors affecting efficiency and stability.
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