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The rise of materials informatics and artificial intelligence (AI)-based computational discovery of materials provides new sources of research directions for materials science. New research demonstrates the enormous potential for experimentalists in photonics for photovoltaic materials to increase the rate of screening and optimization of materials properties and related devices. AI for materials is not only interested in the accuracy of predictive models but also in the effect of data size. Recent investigations have shown significant progress in AI for small data by combining a design of experiments (DoE) approach with machine learning (ML) analysis, which enables experimentalists to use scarce resources more effectively for materials optimization with a higher probability of arriving at a true optimum.
In this work, we propose an alternative approach to DoE associated with ML by using the concept of active learning (AL). AL is well appropriate in industry and physical sciences, where there is a strong need to minimize the number of costly experiments necessary to train predictive models. We focus on optimizing processes of organic photovoltaic (OPV) cells. The manufacturing of OPV devices requires on the case of having a very small labeling budget, about a few dozen data points, and developing a simple and fast method for practical AL with a model selection. Then, we discuss the challenges in anticipating the data-driven process design, such as the complexity of the experimental approach of OPV cells, the diversity of experiment parameters, and the necessary programming ability.
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