Quantum Simulations of Laser-Induced Electron Diffraction using Recurrent Neural Networks

 

 

 

 

 

Quantum Simulations of Laser-Induced Electron Diffraction using Recurrent Neural Networks

Supervisors: Prof. Jochen Küpper (DESY/UHH), Dr. Andrey Yachmenev (DESY), Prof. Armin Iske (UHH)

Strong-field ionization is a versatile and powerful tool to visualize and ultimately control structural changes taking place during chemical reactions and biological processes. Accurate quantum-mechanical simulations of strong field ionization processes are extremely helpful for the analysis of observations, in some cases providing the only means of molecular structure retrieval from such experiments. However, the existing accurate theoretical models are computationally expensive and limited to calculations of relatively small electron excursions, which renders accurate theoretical simulations of laser-induced self-diffraction imaging experiments difficult or practically impossible.
The goal of this project is to develop a computationally efficient quantum mechanical solver for laser-induced strong-field ionization that accurately describes the phenomenon of self-diffraction imaging, including ionization, long-range photoelectron propagation in the laser field, and rescattering with target molecular ion. Our goal is to develop a Recurrent Neural Network framework for representing the time-dependent electron wave function, used to solve the underlying Schrödinger equation. We are looking for a computational scientist with an interest in physics and mathematics or a physicist with a background in computer science. Candidates should have experience with quantum mechanics, machine learning, and programming in Python/Julia/Swift.

Apply here!