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

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Authors: Hee Jung Kim, Wenning Chen, Jae Myeong Lee, Hyun Suk Jung
Cited by: 1
References: 93

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