Issue 4, 2024

Effectiveness and limitation of the performance prediction of perovskite solar cells by process informatics

Abstract

Perovskite solar cells have garnered significant interest owing to their low fabrication costs and comparatively high power conversion efficiency (PCE). The performance of these cells is influenced not solely by material composition but also by experimental processes, rendering PCE prediction a challenging endeavor. It is also crucial to quantitatively assess the impact of process conditions on performance. In this work, we developed machine learning regression incorporating process information derived from an open-access perovskite database. Our analysis showed that the split of process information influenced the prediction accuracy and clarified the relative contribution of each process condition. The limitation of performance prediction was also prone to data degeneracy. The insights gained from this work may facilitate the data-driven design of innovative perovskite solar cells.

Graphical abstract: Effectiveness and limitation of the performance prediction of perovskite solar cells by process informatics

Supplementary files

Article information

Article type
Paper
Submitted
19 dek 2023
Accepted
07 mar 2024
First published
08 mar 2024
This article is Open Access
Creative Commons BY license

Energy Adv., 2024,3, 812-820

Effectiveness and limitation of the performance prediction of perovskite solar cells by process informatics

R. Fukasawa, T. Asahi and T. Taniguchi, Energy Adv., 2024, 3, 812 DOI: 10.1039/D3YA00617D

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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