{"id":95480,"date":"2023-11-21T16:25:43","date_gmt":"2023-11-21T15:25:43","guid":{"rendered":"https:\/\/www.sonnenseite.com\/?p=95480"},"modified":"2023-11-21T16:25:44","modified_gmt":"2023-11-21T15:25:44","slug":"machine-learning-fast-forwards-solar-cell-design","status":"publish","type":"post","link":"https:\/\/www.sonnenseite.com\/en\/science\/machine-learning-fast-forwards-solar-cell-design\/","title":{"rendered":"Machine learning fast-forwards solar cell design"},"content":{"rendered":"\n
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.<\/p>\n\n\n\n
Study lead author Dr Nastaran Meftahi, from RMIT University\u2019s School of Science, said teams of researchers worldwide were racing to make perovskite cells, which were cheaper than silicon and \u2013 thanks to recent advances \u2013 now stable enough for long-term commercial use.<\/p>\n\n\n\n
\u201cUntil now, the process of creating perovskite cells has been more like alchemy than science \u2013 record efficiencies have been reached, but positive results are notoriously difficult to reproduce,\u201d she said.<\/p>\n\n\n\n
\u201cWhat 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.\u201d<\/p>\n\n\n\n
Members of the Centre of Excellence in Exciton Science based at RMIT, Monash University and Australia\u2019s national science agency CSIRO have removed human error from the equation in rapidly innovating solar cells with AI.<\/p>\n\n\n\n
Using data generated by the team\u2019s system, Meftahi, Dr Andrew Christofferson and Professor Salvy Russo from RMIT developed a new model of machine learning.<\/p>\n\n\n\n
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.<\/p>\n\n\n\n
Surmiak and Professor Udo Bach at the Australian Centre for Advanced Photovoltaics and CSIRO will lead this new facility, which is currently under construction.<\/p>\n\n\n\n
Designing reproducible solar cells<\/h4>\n\n\n\n
The team\u2019s combined work, published in top journal Advanced Energy Materials<\/em>, has resulted in reproducible perovskite solar cells with power-conversion efficiencies of 16.9% \u2013 the best-known result manufactured without human intervention.<\/p>\n\n\n\n
\u201cA reproducible 16.9% power-conversion efficiency is better than an irreproducible 30%,\u201d Meftahi said.<\/p>\n\n\n\n
Reproducibility has been a major challenge for human-led and other reported AI-driven perovskite cell design and development processes.<\/p>\n\n\n\n
\u201cCritically, our machine learning model represents the starting point for further optimisation, both in terms of power-conversion efficiency and stability.\u201d<\/p>\n\n\n\n
Surmiak\u2019s 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.<\/p>\n\n\n\n
\u201cThen Adam, with the help of his group, developed 100 new solar cells and that let me predict the properties of 16,000,\u201d Meftahi said.<\/p>\n\n\n\n
\u201cAt Monash, they’ll soon be able to make 2,000 unique solar cells per day. We’re quickly getting to the stage where we\u2019ll be able to predict the properties of millions of different cells.<\/p>\n\n\n\n
\u201cAnd you can’t do that with anybody else’s machine learning model, because you’d need additional information before you’ve made the cell.\u201d<\/p>\n\n\n\n
Next steps<\/h4>\n\n\n\n
Meftahi said the machine-learning model and automated system can also potentially be used to crunch the numbers and run tests on other types of solar cells, including those made with silicon or organic materials.<\/p>\n\n\n\n
\u201cWe are keen to work with partners in industry to further test and prototype our work so that it can be possibly commercialised in a range of applications,\u201d she said.<\/p>\n\n\n\n
Support for the research<\/h4>\n\n\n\n
The Australian Renewable Energy Agency (ARENA), the Australian Centre for Advanced Photovoltaics (ACAP) and the Australian Research Council (ARC) through the ARC Centre of Excellence in Exciton Science supported the work. The researchers also acknowledge the support of the Melbourne Centre for Nanofabrication, Monash X-Ray Platform and Monash Centre for Electron Microscopy.<\/p>\n\n\n\n