article · Engineering Research Express
High heat and humidity in tropical climates often degrade perovskite solar cells, limiting their practical deployment. A climate-aware computational framework combines density functional theory, atomistic simulations, and machine learning to screen and optimise caesium lead iodide perovskite compositions. First-principles calculations established the structural, electronic, and thermodynamic properties of pristine and modified materials, whilst molecular dynamics examined lattice behaviour and water interactions. A graph neural network accurately predicted material bandgaps, and a random forest model ranked material suitability against tropical environmental factors including temperature, humidity, and solar irradiance. The analysis identified temperature, defect formation energy, and ion migration barriers as the primary drivers of performance stability. Screening highlighted two specific compositions, caesium formamidinium lead iodide and caesium lead iodide bromide, with estimated device efficiencies between 21% and 26%, providing a theoretical guide for targeted material synthesis.
Perovskite solar cells offer high potential for low-cost solar power, but hot and humid environments often trigger rapid degradation. By integrating environmental data directly into computational materials screening, this research helps identify stable chemical compositions before undertaking expensive laboratory fabrication, speeding up the design of solar technologies tailored to tropical regions.
This computational framework aids photovoltaic materials developers and solar technology manufacturers seeking durable formulations for tropical deployment. By shortlisting candidates such as Cs0.1FA0.9PbI3 and CsPbI2Br, it narrows the chemical search space for experimentalists. Because all findings derive entirely from computational models and reduced-order device simulations without laboratory prototyping, this work remains early-stage research requiring full experimental synthesis and physical device validation before commercial development can begin.
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Abstract Climate-induced temperature and humidity variations pose significant challenges to the development of stable and efficient perovskite photovoltaic materials suitable for tropical environments. This study presents a climate-aware artificial intelligence–density functional theory (AI–DFT) computational framework for the accelerated screening and optimization of caesium lead iodide (CsPbI 3 )-based perovskite absorbers by integrating first-principles calculations, machine learning (ML), atomistic stability analysis, and reduced-order device modelling. DFT calculations were employed to evaluate the structural, electronic, optical, thermodynamic, and defect-related characteristics of pristine and compositionally modified CsPbI 3 systems. The optimized cubic CsPbI 3 structure exhibits a lattice constant of 6.28 Å, a direct bandgap of 1.48 eV, a formation energy of −1.238 eV atom −1 , and an energy above hull of 0.025 eV atom −1 , indicating favourable characteristics relevant to photovoltaic applications. Finite-temperature ab initio molecular dynamics, phonon calculations, and reactive molecular dynamics simulations provide complementary insights into lattice behaviour and initial surface–water interaction mechanisms under the investigated conditions. The atomistic line graph neural network was trained and independently evaluated using a labelled dataset of 1024 structures, achieving a test mean absolute error of 0.0189 eV and an R 2 value of 0.97 for bandgap prediction. A random forest model subsequently integrated intrinsic material descriptors with temperature, relative humidity, and solar irradiance to generate a climate suitability ranking for candidate materials under tropical operating conditions. Perturbation-based sensitivity analysis revealed that temperature (24.5%), defect formation energy (21.8%), and ion-migration barrier (18.6%) were among the dominant factors affecting the predicted suitability index. AI-guided screening identified Cs 0.1 FA 0.9 PbI 3 and CsPbI 2 Br as promising absorber compositions with complementary efficiency–stability characteristics. A DFT-informed reduced-order photovoltaic model estimated device-level parameters, with predicted efficiencies of 21%–26% interpreted as physics-informed screening indicators rather than experimentally validated device efficiencies. Overall, the proposed AI–DFT framework provides a physically interpretable computational approach for climate-aware prioritization of perovskite photovoltaic materials by connecting quantum-derived descriptors, ML predictions, environmental factors, sensitivity-based robustness assessment, and device-level performance estimation. The framework is intended to guide future experimental validation, advanced device simulations, and climate-specific photovoltaic material development under tropical operating conditions.
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DOI: 10.1088/2631-8695/ae9cfe
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