Members of the Centre of Excellence in Exciton Science based at RMIT, Monash University and Australia’s national science agency CSIRO have removed human error from the equation in rapidly innovating solar cells with AI.
Using data generated by the team’s system, Meftahi, Dr Andrew Christofferson and Professor Salvy Russo from RMIT developed a new model of machine learning.
With a multimillion-dollar automated system for solar cell manufacturing being built by Dr Adam Surmiak at Monash University, the model will be capable of predicting huge volumes of promising chemical recipes for new perovskite solar cells.
Surmiak and Professor Udo Bach at the Australian Centre for Advanced Photovoltaics and CSIRO will lead this new facility, which is currently under construction.
Designing reproducible solar cells
The team’s combined work, published in top journal Advanced Energy Materials, has resulted in reproducible perovskite solar cells with power-conversion efficiencies of 16.9% – the best-known result manufactured without human intervention.
“A reproducible 16.9% power-conversion efficiency is better than an irreproducible 30%,” Meftahi said.
Reproducibility has been a major challenge for human-led and other reported AI-driven perovskite cell design and development processes.
“Critically, our machine learning model represents the starting point for further optimisation, both in terms of power-conversion efficiency and stability.”
Surmiak’s team designed and characterised 16 new solar cells never seen before using his novel setup, and Meftahi used these cells to predict the properties of 256 new solar cell recipes.
“Then Adam, with the help of his group, developed 100 new solar cells and that let me predict the properties of 16,000,” Meftahi said.
“At Monash, they'll soon be able to make 2,000 unique solar cells per day. We're quickly getting to the stage where we’ll be able to predict the properties of millions of different cells.
“And you can't do that with anybody else's machine learning model, because you'd need additional information before you've made the cell.”
