AI-driven green processing and life cycle assessment for sustainable perovskite solar cells
Abstract
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. This perspective highlights how green solvents, artificial intelligence, and life cycle assessment can be integrated to guide perovskite solar cells toward safer processing, lower environmental impact, and more sustainable commercialization.
Fields of Study & Subjects
Perovskite Materials and Applications
Machine Learning in Materials Science
Photovoltaic Systems and Sustainability
Publication details
- Journal / source
- Nature Communications
- Publisher
- Springer Science and Business Media LLC
- Volume
- 17
- Issue
- 1
- Publication date
- 20 May 2026
- Language
- EN