Computational Design of Metal-Free Porphyrin Dyes for Sustainable Dye-Sensitized Solar Cells with Perspectives towards Energy-Aware Decision Support

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Abstract Metal-free porphyrin dye-sensitized solar cells (DSSCs) are promising low-cost and sustainable photovoltaic technologies, but their integration into energy-related decision processes is hindered by the lack of reliable, early-stage performance data. This study presents a high-performance-computing-enabled in-silico screening pipeline that links molecular-level dye design to techno-economic indicators and energy-aware decision support. We systematically design and evaluate fifteen metal-free porphyrin D-π-A dyes by combining five donor units with three anchoring groups, including mono- and bi-dentate motifs. Density functional theory (DFT) and time-dependent DFT (TD-DFT) are used to compute optoelectronic and photovoltaic performance indicators, including HOMO-LUMO alignment, absorption spectra, charge-transfer energetics (ΔGinj, ΔGreg), open-circuit voltage (VOC), short-circuit current (JSC), and power conversion efficiency (PCE). The screening identifies a top-performing dye (N1) with a predicted PCE of 14.37%, combining high voltage and efficient charge injection. Beyond materials discovery, this study highlights the role of high-performance computing in enabling predictive screening of dye candidates and generating structured performance indicators (HOMO-LUMO gaps, absorption spectra, charge-transfer free energies, photovoltaic metrics). These outputs can be interpreted as early-stage descriptors that inform energy informatics workflows and support exploratory techno-economic assessments. By positioning molecular-level predictions within a broader computational pipeline, the study illustrates how such data can be progressively translated into inputs for scenario-based analysis and energy-aware decision support. Overall, the work contributes to bridging computational chemistry and energy informatics by defining a scalable, data-driven pathway from molecular design to decision-relevant insights, supporting innovation in sustainable photovoltaic technologies and long-term energy transitions.

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