I am a student in the CDT in Computational Methods for Materials Science at Cambridge. Currently I am in the MPhil in Scientific Computing, which is the first year of the CDT.
I graduated in 2014 from the University of Minnesota with a Bachelor of Science in physics and in computer science. I also did an exchange year at the TU Munich from 2011-2012. My research so far has been in various applications of computing to physics: During a summer fellowship at Los Alamos National Laboratory in 2013 I analyzed Monte Carlo simulations of nuclear reactors. During my last year at the U of M I worked on simulating genetic regulatory networks in cells. Now, in Dr. Gábor Csányi's research group, I will be taking machine learning atomistic simulation methods into a new area by applying them to materials with strong electrostatic interactions. Examples include hydrocarbon compounds mixed with water and most biological molecules. A machine learning method uses accurate quantum mechanical calculations to inform much faster molecular dynamics simulations, which can be used to calculate a material's macroscopic properties. However, in systems with strong electrostatic interactions, the long length scales over which these interactions take place make machine learning difficult. The goal of my project is to use machine learning to construct an electrostatic force field that will account for these interactions. This research will potentially enable the calculation of the macroscopic properties of these materials from first principles.