Analysis of degradation in perovskite solar cells through physics-based machine learning

Kjeld O. Jensen; Gemma Giliberti; Aldo Di Carlo; Will Clarke; Giles Richardson; Taylor Blackwell; Petra J. Cameron; Alison B. Walker

arXiv · 2608.10691

Analysis of degradation in perovskite solar cells through physics-based machine learning

Kjeld O. Jensen·Gemma Giliberti·Aldo Di Carlo·Will Clarke·Giles Richardson·Taylor Blackwell·Petra J. Cameron·Alison B. Walker

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Abstract

Degradation in lead halide perovskite solar cells is analysed by inverse modelling of published measurements of characteristics of a single solar cell at ages 0, 90, 280, 480 minutes. We employ machine learning to deduce distributions of material parameter values and hence the physics linked to measured changes. Bayesian parameter estimation is coupled with drift diffusion simulations using the IonMonger code combined with an optical model. We accurately replicated measured changes in device performance with age through variations in model input parameters. Our key result is that degradation is influenced by correlated changes in the concentrations and diffusion coefficients of mobile ions and by interface recombination at large mobile ion concentrations. This study demonstrates the power of machine learning combined with simulations to reliably interpret experimental results, a task which is problematic if using simulation models with only manual exploration of the input parameter space.

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Kjeld O. Jensen, Gemma Giliberti, Aldo Di Carlo, Will Clarke, Giles Richardson, Taylor Blackwell, Petra J. Cameron, Alison B. Walker. 2026-08-11. Analysis of degradation in perovskite solar cells through physics-based machine learning. https://arxiv.org/abs/2608.10691

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