Researchers led by China's Nankai University have explored ChatGPT's ability to generate hypotheses for material science and identify untested molecules capable of reducing surface recombination and thereby boosting the efficiency of perovskite solar cells.
“We are still not anything near the Jarvis system from the Iron Man movies, but we are really getting closer, and what we show in this paper is that we now are sufficiently close for the system to actually be useful for generating hypotheses,” corresponding author T. Jesper Jacobsson told pv magazine. “Admittedly, it required a bit of luck, some domain knowledge, and that we gave the right type of question, but nevertheless, it worked.”
To find molecules for surface passivation in hybrid perovskite solar cells with a p-i-n architecture, the group used ChatGPT 3.5, with a data cutoff in September 2021. Comparing their interaction with the chat model to brainstorming, the group said it was an ongoing dialog and exchange of questions and answers. Among requests they input into the large language model (LLM), for example, there was information on commercially available compounds, or at least easily synthesizable, reasonably priced, and not overly toxic.
However, ChatGPT did not do everything. The scientists manually verified the general plausibility of the suggested materials, later checking in academic databases whether they had already been explored. Through this process, they identified polyallylamine (PAA).
Image: Nankai University, Cell Reports Physical Science, CC BY 4.0
“PAA is a water-soluble biodegradable polymer with applications in areas such as medicine, nanoparticle synthesis, and heavy metal ions chelating,” the research group explained. “ChatGPT provided us with suggestions for other molecules as well, but based on price, availability, toxicity, structural similarity to other surface passivation, and a lack of previous reports using PAA for this purpose, we decided that PAA would be an interesting molecule to explore experimentally.”
Following up with a real-world experiment, the scientist manufactured 125 devices. The device structure followed a standard p-i-n architecture composed of a soda lime glass/indium tin oxide (SLG/ITO) substrate, a MeO-2PACz hole transport layer, a PCBM-60/BCP electron transport layer, and silver metal contact. The absorber was based on a triple cation Cs0.05FA0.91MA0.04PbBr0.15I2.85 perovskite with a band gap of 1.54 eV.
A thin layer of PAA was applied onto the perovskite film by spin coating before the deposition of the PCBM layer. In some of the experiments, the PAA solution was 0.015% in isopropyl alcohol (IPA), while in others, it was 0.025% or 0.05%. Control samples without PAA treatment were also manufactured.
Popular content
“The average device performance increased by around 2 percent units, with a top performance of 22.75%,” said the researchers. “This outcome offers a compelling demonstration of the value inherent in human-AI collaboration.”
The results were presented in “The use of ChatGPT to generate experimentally testable hypotheses for improving the surface passivation of perovskite solar cells,” published in Cell Reports Physical Science. The team included scientists from China's Nankai University and Sweden's Linköping University.
This content is protected by copyright and may not be reused. If you want to cooperate with us and would like to reuse some of our content, please contact: editors@pv-magazine.com.
Perfect use of AI… for the benefit of mankind… than its “public image” of its destructive capabilities…. WELL DONE… !!!!…..