Post by Stephanie Law

I'm delighted to share our recent article on using machine learning to discover molecular beam epitaxy growth conditions for polymorph-pure In2Se3 published in Journal of Vacuum Science & Technology A with co-authors Ryan Trice, Mingyu Yu, Eric Welp, Morgan Applegate, and Wesley Reinhart! In this paper, we describe how ML can be used to help researchers find optimal synthesis conditions even in unintuitive growth regimes and discover synthesis-structure relationships. This was one of our first forays into using ML for materials synthesis, and I'm very excited to continue in this direction. Special thanks to the Penn State University Materials Research Institute for providing the seed funding for this effort and the Two-Dimensional Crystal Consortium user facility.