Issue 15, 2024

Combining component screening, machine learning and molecular engineering for the design of high-performance inverted perovskite solar cells

Abstract

Achieving high-performance inverted perovskite solar cells (PSCs) still remains a significant challenge, necessitating innovative approaches in materials selection and manufacturing technique optimization of perovskites. In this work, we unveil a paradigm shift in PSCs optimization. Through a judicious selection from a repertoire of 60 perovskite variants, we identified a composition with exemplary optical, thermal and electrical stability. Employing Bayesian machine learning, we navigated a labyrinth of over 1 billion process conditions, culminating in a record-breaking efficiency within a mere 80 iterations. Finally, the integration of bespoke in situ polymerized ionic molecules allowed us to further augment performance of inverted PSCs, reaching an unparalleled power conversion efficiency of 25.76% (certified at 25.21%). The PSCs retained 94% of the initial efficiency after continuous operation in a nitrogen atmosphere at 65 °C for 1920 hours. This work not only redefines the benchmarks for PSCs but also illuminates the path forward for photovoltaic innovations.

Graphical abstract: Combining component screening, machine learning and molecular engineering for the design of high-performance inverted perovskite solar cells

Supplementary files

Article information

Article type
Paper
Submitted
07 Feb 2024
Accepted
26 Jun 2024
First published
02 Jul 2024

Energy Environ. Sci., 2024,17, 5532-5541

Combining component screening, machine learning and molecular engineering for the design of high-performance inverted perovskite solar cells

B. Zhang, H. Zeng, H. Yin, D. Zheng, Z. Wan, C. Jia, T. Stuyver, J. Luo and T. Pauporté, Energy Environ. Sci., 2024, 17, 5532 DOI: 10.1039/D4EE00635F

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