Advances in computational materials design have enabled rapid screening for desirable properties of both real and virtual compounds. These successes in accelerated materials design have moved the bottleneck in materials development towards the synthesis of novel compounds, and much of the momentum and efficiency gained in the design process becomes gated by trial‐and‐error synthesis techniques. This research will revolve around exploring, and building predictive tools for synthesis routes of materials using machine learning. The study will include developing an adequate data structure to describe materials synthesis routes, which can be often complex and multifaceted, as well as the machine learning techniques for synthesis route prediction.

I’m a course 6-3 hailing from NYC. My project is just an amazing opportunity for me to learn more and actively use my skills to create something that can change people’s lives. I’m very excited to do superUROP.