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<p>Despite rapid advances in perovskite solar cells, solvent selection remains a central determinant of safety, process robustness, and end-of-life outcomes. These constraints are multi-dimensional and involve competing trade-offs, making them challenging to resolve through experimental optimization alone. This Perspective integrates green solvent engineering with artificial intelligence (AI) and life cycle assessment (LCA) to provide a unified sustainability framework. We discuss solvent-precursor coordination and processing-window robustness as governing factors. We also highlight how AI can accelerate solvent discovery and reduce key life&#xa0;cycle inventory gaps, while LCA quantifies trade-offs and mitigates burden shifting. This combined lens clarifies sustainability-relevant priorities for the field.</p>

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AI-driven green processing and life cycle assessment for sustainable perovskite solar cells

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月20日

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  • Hee Jung Kim,
  • Wenning Chen,
  • Jae Myeong Lee,
  • Hyun Suk Jung

摘要

Despite rapid advances in perovskite solar cells, solvent selection remains a central determinant of safety, process robustness, and end-of-life outcomes. These constraints are multi-dimensional and involve competing trade-offs, making them challenging to resolve through experimental optimization alone. This Perspective integrates green solvent engineering with artificial intelligence (AI) and life cycle assessment (LCA) to provide a unified sustainability framework. We discuss solvent-precursor coordination and processing-window robustness as governing factors. We also highlight how AI can accelerate solvent discovery and reduce key life cycle inventory gaps, while LCA quantifies trade-offs and mitigates burden shifting. This combined lens clarifies sustainability-relevant priorities for the field.