I’m a postdoctoral researcher in the Department of Mathematics and Computer Science at FU Berlin. My current research focuses on the development of DeepQMC (PauliNet) - a Python package which achieves nearly exact solutions of the Schrödinger equation for molecular systems through the use of deep neural networks.
I completed my PhD in Physics at the University of York (UK) which focused on the fundamentals of many-electron physics in matter, specifically, investigating the exact functionals of time-dependent density-functional theory.
PhD in Physics, 2020
University of York
BSc (Hons) in Theoretical Physics, 2015
University of York
Shortly after completing my PhD I moved to Germany to join Prof. Frank Noé’s group at FU Berlin. His interdisciplinary AI4Science Group is conducting state-of-the-art research in the development of machine learning methods for problems arising in the natural sciences.
My research focuses on the development of DeepQMC - a Python package which implements variational quantum Monte Carlo for electrons in molecules, using deep neural networks written in PyTorch as trial wave functions. The recent success of PauliNet, created by Hermann et al., demonstrates the huge potential of the DeepQMC approach.
Responsibilities included:
Thesis: Characterising and approximating exact density functionals for model
electronic systems
Supervisor: Prof. Rex Godby
Responsibilities included: