Discovering molecules and processed with optimized performance for emerging PV Technologies
Christoph Brabec a b
a Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, FAU, Martensstrasse 7, 91058 Erlangen, Germany
b Forschungszentrum Jülich GmbH, Helmholtz-Institute Erlangen-Nürnberg (HI ERN),91058 Erlangen, Germany
International Conference on Hybrid and Organic Photovoltaics
Proceedings of International Conference on Hybrid and Organic Photovoltaics (HOPV24)
València, Spain, 2024 May 12th - 15th
Organizer: Bruno Ehrler
Invited Speaker, Christoph Brabec, presentation 023
DOI: https://doi.org/10.29363/nanoge.hopv.2024.023
Publication date: 6th February 2024

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 5 & 6 dimensional spaces, the possible variations go into the millions. Nevertheless, our automated lines, being operated in an autonomous optimization mode, were able to identify globalized optima within several hundred´s of experiments. That raises the question whether these large material spaces as well hold the promise for discoveries. We extended the BO concept towards the discovery of new molecules that can be integrated into the device optimization cycle. The research campaign found molecular semiconductors that had not been published before but yielded performance values bypassing the current state of the art materials.

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