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.
Researchers use a methodology based on combinatorial screening in which they generate samples with gradients in the parameters that mostly affect the performance of organic solar cells (i.e. composition and thickness).
Artificial intelligence algorithms in the field of materials science are mainly used to look for behavior patterns and to further develop predictive models of the behavior of a family of materials for a given application. To do so, an algorithm is first trained by exposing it to real data to generate a model algorithm. The model is then validated with other data points not used to create the model, but from the same category of materials. Once validated, the algorithm is applied to predict the behavior of other similar materials that are not part of the training nor validating set.
This work represents two great achievements. On the one hand, developing AI models that predict how efficiency depends on many of the organic solar cell parameters. The degree of prediction is very high even for materials that have not been used in the training set.
Researchers believe that the results and the methodology developed in this study are very important to guide theoretical researchers as to what to take into account when developing future analytical models that attempt to determine the efficiency of a given system.