Faster to Better Photovoltaic Materials with AI

December 13, 2024

Perovskite solar cells are considered a flexible and sustainable alternative to conventional silicon-based solar cells. Within just a few weeks, researchers have now discovered new organic molecules that can be used to increase the efficiency of perovskite solar cells. The international team also includes scientists from the Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (HI ERN), a branch of Forschungszentrum Jülich. The scientists skillfully combined the use of AI with fully automated high-throughput synthesis. The strategy developed can be transferred to other areas of materials research, such as the search for new battery materials. The results of the research work were published in the renowned journal Science.

Mit KI schneller zu besseren Photovoltaik-Materialien — Dr. Jianchang Wu (HI ERN) is part of the international research team that has developed a new strategy in the field of materials research.

If you want to find the one molecule in a million that makes perovskite solar cells particularly efficient as conductors of positive charge, you have to produce and test these million molecules - or you have to do as the researchers around Professor Pascal Friederich from the Institute of Nanotechnology at KIT and Professor Christoph Brabec from HI ERN have done. "With only 150 targeted experiments, a breakthrough was achieved that would otherwise have required hundreds of thousands of tests. The developed workflow opens up new possibilities for the rapid and cost-effective discovery of high-performance materials in a wide range of applications," says Brabec. With one of the materials discovered in this way, they increased the efficiency of a reference solar cell by about two percent to 26.2 percent. "This success shows that a clever strategy can save an enormous amount of time and resources in the development of new energy materials," says Friederich.

The starting point at HI ERN was a database with the structural formulas of around one million virtual molecules that could be produced from commercially available substances. Using established quantum mechanical methods, the researchers at KIT calculated the energy levels, polarity, geometry and other characteristics of 13,000 of these randomly selected virtual molecules.

AI training with data from just 101 molecules

From these 13,000 molecules, the researchers selected 101 molecules that differed as much as possible in terms of their characteristics. These were automatically produced at HI ERN with the help of a robotic system and used to manufacture otherwise identical solar cells. They then measured their efficiency. "Thanks to our highly automated synthesis platform, it was crucial for the success of our strategy that we produced truly comparable samples and thus determined reliable values for the efficiency," says Christoph Brabec, who led the work at HI ERN.

The KIT researchers trained an AI model using the efficiencies achieved and the characteristics of the associated molecules. The model then suggested a further 48 molecules for synthesis based on two criteria: an expected high efficiency and unpredictable properties. "If the machine learning model is unsure about the predicted efficiency, it is worth producing the molecule to examine it more closely," says Pascal Friederich, explaining the second criterion. "It could surprise us with a high degree of efficiency."

In fact, the molecules proposed by the AI could be used to build solar cells with above-average efficiency, including those that outperform other state-of-the-art materials. "We can't be sure that we have really found the best of a million molecules, but we are certainly close to the optimum," says Friederich.

AI versus chemical intuition

The researchers can understand the AI's molecular suggestions to a certain extent, as the AI used indicates which characteristics of the virtual molecules were decisive for its suggestions. It turned out that the AI suggestions were also partly based on characteristics that chemists had previously paid less attention to, for example the presence of certain chemical groups such as amines.

Christoph Brabec and Pascal Friederich are convinced that their strategy is also promising for materials research in other areas of application or can be extended to the optimization of entire components.

The research results, which were obtained in collaboration with researchers from the University of Erlangen-Nuremberg, the South Korean Ulsan National Institute of Science, the Chinese Xiamen University and the University of Electronic Science and Technology in Chengdu, China, were recently published in the renowned journal "Science".

^{Dr. Jianchang Wu (HI ERN) about the skillfull combination of AI with fully automated high-throughput synthesis.}
^{(Video on Youtube, Duration: 1:48 min.)}
_{Copyright: Kurt Fuchs/HI ERN}

Original Publication

Jianchang Wu, Luca Torresi, ManMan Hu, Patrick Reiser, Jiyun Zhang, Juan S. Rocha-Ortiz, Luyao Wang, Zhiqiang Xie, Kaicheng Zhang, Byung-wook Park, Anastasia Barabash, Yicheng Zhao, Junsheng Luo, Yunuo Wang, Larry Lüer, Lin-Long Deng, Jens A. Hauch, Dirk M. Guldi, M. Eugenia Pérez-Ojeda, Sang Il Seok, Pascal Friederich, Christoph J. Brabec: Inverse design of molecular hole-transporting semiconductors tailored for perovskite solar cells. Science, 2024. DOI 10.1126/science.ads0901.

About HI ERN

The Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (HI ERN) researches and develops material and process-based solutions for the climate-neutral, sustainable and cost-effective utilization of renewable energies. The institute focuses on electrochemical energy conversion for the development of innovative hydrogen and solar technologies.

HI ERN is the core of a close partnership between Forschungszentrum Jülich, Helmholtz Zentrum Berlin für Materialien und Energie and Friedrich-Alexander University Erlangen-Nuremberg. The aim of the cooperation is to closely link the excellent materials, energy and process research of the partner institutions. The cooperation between the partners focuses on innovative materials and processes for photovoltaic energy systems and hydrogen as a storage and carrier medium for CO2-neutral energy. The HI ERN makes an important contribution to the energy transition through interdisciplinary cooperation (more).