Machine learning can be a powerful tool to prescreen new materials, thus accelerating the development of the OPV field.

Third-generation solar cells are designed to achieve high power-conversion efficiency while being low-cost to produce. These solar cells have the ability to surpass the Shockley–Queisser limit. This review focuses on different types of third-generation solar cells such as dye-sensitized solar cells, Perovskite-based cells, organic photovoltaics, quantum dot solar cells, and tandem solar cells, a stacked form of different materials utilizing a maximum solar spectrum to achieve high power conversion efficiency. Apart from these solar cells, other third-generation technologies are also discussed, including up-conversion, down-conversion, hot-carrier, and multiple exciton. This review provides an overview of the previous work in the field, alongside an introduction to the technologies, including their working principles and components. Advancements made in the different components and improvements in performance parameters such as the fill factor, open circuit voltage, conversion efficiency, and short-circuit current density are discussed. We also highlight the hurdles preventing these technologies from reaching commercialization.

The selection of printing parameters for 3D printing can dramatically affect the dynamic performance of components such as polymer spur gears. In this paper, the performance of 3D printed gears has been optimised using a machine learning process. A genetic algorithm (GA)–based artificial neural network (ANN) multi-parameter regression model was created. There were four print parameters considered in 3D printing process, i.e. printing temperature, printing speed, printing bed temperature and infill percentage. The parameter setting was generated by the Sobol sequence. Moreover, sensitivity analysis was carried out in this paper, and leave-one cross validation was applied to the genetic algorithm-based ANN which showed a relatively accurate performance in predictions and performance optimisation of 3D printed gears. Wear performance of 3D printed gears increased by 3 times after optimised parameter setting was applied during their manufacture.