Machine Learning Predicts the Performance of Organic Solar Cells

Results and the methodology developed in this study are very important to guide theoretical researchers to develop future analytical models.

FREMONT, CA: Now, researchers from the Institute of Materials Science of Barcelona, specialized in materials for energy applications, have collaborated with researchers from the Universitat Rovira I Virgili specialized in Artificial Intelligence, to combine the experimental data points that they gather with artificial intelligence algorithms and enable an unprecedented predicting capability of the performance of organic solar cells.

ICMAB researchers, led by Mariano Campoy-Quiles, have generated multiple data sets by using a new experimental method that allows them to have a large number of samples in only one, speeding the time compared to conventional methods. Then, machine-learning models are used to learn from those data sets and predict the performance of even more materials, such as novel organic semiconductors synthesized at the group of Prof. Martin Heeney at Imperial College London.

This study may be the first of many in the field to combine artificial intelligence and high-throughput experiments to predict the optimum conditions of certain materials and devices.

One of the key aspects of this study is that researchers can generate big and meaningful datasets with minimal experimental effort. This is an important aspect of the success of machine-learning modeling to obtain accurate and reliable models and predictions.

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