Proceedings of MATSUS Spring 2024 Conference (MATSUS24)
DOI: https://doi.org/10.29363/nanoge.matsus.2024.349
Publication date: 18th December 2023
Organic or perovskite photovoltaics poses a multi-objective optimization problem in a
large multi-dimensional parameter space. Massive progress was achieved in developing
methods to accelerate solving such complex optimization tasks. We have demonstrated
for both types of semiconductors, that the combination of Gaussian Process Regression
(GPR) and Bayesian Optimization (BO) are most efficient in predicting new materials,
identify optimized processing conditions or invent alternative device architectures in
larger parameter rooms. For a 4 dim space (solvent, donor-acceptor ratio, spin speed,
concentration) with about 1000 variations in a 10 % grid space, 30 samples are sufficient
to find the optimum. For 6 dimensional spaces, the possible variations go into the
millions and billions. Nevertheless, our automated lines, being operated in an
autonomous optimization mode, were able to identify globalized optima within several
hundred´s of experiments. In the outlook we discuss whether these autonomously
operated research lines can as well handle unorthodox optimization problems such as
the recycling of organic or perovskite solar cells
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