The number of materials with the potential for use in each of the many layers in a solar cell is enormous. And even once they have chosen one to work with, scientists need to understand its interactions with the other materials present, and the effects of changing parameters such as layer thickness, dopant concentration and a wealth of others in order to get the best out of the cells they are working on. With so many possibilities, this can be a time-consuming process. And scientists today are increasingly able to turn to artificial intelligence to guide them in the next steps to take in practical lab work. And developing a system to do just that for solar cell design was the focus of a group of researchers at the Massachusetts Institute of Technology (MIT), who worked with experts at Google Brain to develop a system to evaluate the potential of different solar cell designs, and also predict which changes would provide improved performance characteristics.