Advanced, Manufacturing News

Researchers use AI to advance solar cell production

Researchers in Australia have harnessed AI to produce solar cells from the mineral perovskite in just a matter of weeks

Researchers in Australia have harnessed AI to produce solar cells from the mineral perovskite in just a matter of weeks, bypassing years of human labour and human error to optimise the cells.

Study lead author Dr Nastaran Meftahi, from RMIT University’s School of Science, said teams of researchers worldwide were racing to make perovskite cells, which were cheaper than silicon and – thanks to recent advances – now stable enough for long-term commercial use.

“Until now, the process of creating perovskite cells has been more like alchemy than science – record efficiencies have been reached, but positive results are notoriously difficult to reproduce,” she said.

“What we have achieved is the development of a method for rapidly and reproducibly making and testing new solar cells, where each generation learns from and improves upon the previous.”

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.

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 per cent – the best-known result manufactured without human intervention.

“A reproducible 16.9 per cent power-conversion efficiency is better than an irreproducible 30 per cent,” 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.”

Send this to a friend