Charlie Muston

The emerging field of computational materials synthesis promises to close the gap between the abundance of candidate functional materials and the few that we can make.

Today's computers and machine-learning methods can simulate atoms with quantum-mechanical accuracy at scales large enough to model functional materials. I am joining the Materials Theory Group, which has pioneered structure-searching methods that find the low-energy arrangements of atoms in materials such as superconductors and metal-organic frameworks. With this foundation, my work will increase the complexity of the systems whose stable phases we can predict. Ultimately, I aim to simulate chemical change and find ways to make new materials.

Who or what inspired you to pursue your research interests?

A summer in Professor Volker Deringer's group introduced me to computational materials science. I was excited by his group's work at the forefront of science and surprised by the length scales accessible with machine learning. For my master's project, I pursued my interest in solving electronic structure problems. My supervisor, Professor John McGrady, inspired me with his ability to attribute chemistry to a small number of orbitals. Enjoying this measured approach to molecules, I looked to take it to larger length scales. I found computational materials synthesis at a seminar, in a disagreement between frustrated experimentalists and an optimistic theoretician.