AI trained on future solar
Australian researchers have developed a new machine learning program to speed up clean energy generation.
Researchers at the ARC Centre of Excellence in Exciton Science have successfully created a new type of machine learning model to predict the power-conversion efficiency (PCE) of materials for use in next-generation organic solar cells, including ‘virtual’ compounds that don’t exist yet.
Unlike some time-consuming and complicated models, the latest approach is quick, easy to use and the code is freely available for all scientists and engineers.
The key to developing a more efficient and user-friendly model was to replace old, complicated and computationally-expensive parameters with simpler chemical signature descriptors of the molecules being analysed.
The new approach could help to significantly speed up the process of designing more efficient solar cells at a time when the demand for renewable energy, and its importance in reducing carbon emissions, is greater than ever.
The results have been published in the Nature journal Computational Materials.