“These results demonstrate that we can make use of machine learning in identifying candidate materials and suitable conditions to prevent degradation in perovskites.”Marina Leite, University of California, Davis
Researchers at the University of California (UC) Davis’ College of Engineering are using machine learning to identify new materials for high-efficiency solar cells. By combining 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. The report their findings in a paper in ACS Energy Letters.
Hybrid perovskites are organic-inorganic molecules that have received a lot of attention over the past 10 years for their potential use in renewable energy, said Marina Leite, associate professor of materials science and engineering at UC Davis and senior author of the paper. Some are comparable in efficiency to silicon for use in solar cells, but they are cheaper to make and lighter, potentially allowing a wide range of applications, including for light-emitting devices.
A primary challenge in the field is that perovskite devices tend to degrade way more readily than silicon when exposed to moisture, oxygen, light, heat and voltage. This is spurring researchers to find perovskites that combine high-efficiency performance with resilience to environmental conditions.
Perovskites have a general structure of ABX3, where A is an organic (carbon-based) or inorganic group, B is lead or tin, and X is a halide (meaning chlorine, iodine or fluorine, or a combination).
“The number of possible chemical combinations alone is enormous,” Leite said. Furthermore, the perovskites 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. 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 decided to test whether machine-learning algorithms could be effective at predicting the effects of moisture on the degradation of hybrid perovskites.
To do this, Srivastava and Hering built an automated, high-throughput system to measure the photoluminescence efficiency of five different perovskite films exposed to the conditions of summer days in Sacramento. They were able to collect over 7000 measurements in a week, accumulating enough data to form a reliable training set.
They then 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 and found that the SARIMAX model showed the best performance, achieving 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 include expanding the experiments to quantify combinations of multiple environmental factors.
The perovskite film itself is only a part of a complete photovoltaic cell, but 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.”
This story is adapted from material from the University of California, Davis, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier. Link to original source.