Many real-world problems involve datasets where only some of the data is labeled and the rest is unlabeled. In this post, we discuss our implementation of semi-supervised learning for predicting the synthesizability of theoretical materials. When we think about the materials that will enable next-generation technologies, it's probably not the case that there is one ultimate material waiting to be found that will solve all our problems. The problems we need to solve (producing and storing clean energy, mitigating climate change, desalinating water, etc.) are complex and varied. Even zooming in to the next-generation of electronics, computers, and nanotechnology, there probably isn't a single perfect material to exploit in the same way that silicon has been used in all our familiar devices.