Researchers use machine learning to predict optical behavior of halide perovskites with >90% accuracy

Researchers at the University of California, Davis College of Engineering and Georgia Institute of Technology are using machine learning to identify new materials for high-efficiency solar cells. Using high-throughput experiments and machine learning-based algorithms, they have found it is possible to forecast the materials’ dynamic behavior with very high accuracy, without the need to perform as many experiments.

A primary challenge in the field of perovskite-based solar cells is that the perovskite devices tend to degrade faster than silicon when exposed to moisture, oxygen, light, heat, and voltage. The challenge is to find which perovskites combine high-efficiency performance with resilience to environmental conditions. Marina Leite, associate professor of materials science and engineering at UC Davis and senior author of the paper, said that “the number of possible chemical combinations alone is enormous". Furthermore, they need to be assessed against multiple environmental conditions, alone and in combination, which results in a hyperparameter space that cannot be explored using conventional trial-and-error methods. “The chemical parameter space is enormous,” Leite said. “To test them all would be very time consuming and tedious.”


As a first and key step towards solving these challenges, Leite and graduate students Meghna Srivastava and Abigail Hering decide to test whether machine learning algorithms could be effective when testing and predicting the effects of moisture on material degradation. Srivastava and Hering built an automated, high-throughput system to measure the photoluminescence efficiency of five different perovskite films against the conditions of summer days in Sacramento. They were able to collect over 7,000 measurements in a week, accumulating enough data for a reliable training set.

They used this data to train three different machine learning algorithms: a linear regression model, a neural network and a statistical model called SARIMAX. They compared the predictions of the models to physical results measured in the lab. The SARIMAX model showed best performance with a 90% match to observed results during a window of 50-plus hours.

“These results demonstrate that we can make use of machine learning in identifying candidate materials and suitable conditions to prevent degradation in perovskites,” Leite said. Next steps will be to expand the experiments to quantify combinations of multiple environmental factors.  

The perovskite film itself is only a part of a complete photovoltaic cell, Leite said. The same machine learning approach could also be used to forecast the behavior of a complete device. “Our paradigm is unique, and I am eager to see the upcoming measurements. Moreover, I am very proud of the students’ diligence during the pandemic”.

Posted: Apr 24,2023 by Roni Peleg