Coherent Control of Materials Properties

Oscar Grånäs
Oscar Grånäs. Photo: Mikael Wallerstedt.

The Swedish Research Council reached a decision on October 31, 2019 on project grants and starting grants for Natural and Engineering Sciences. The Department of Physics and Astronomy is granted 40 840 000 SEK for the period 2020-2023 for in total nine project grants and three starting grants. The projects will begin during 2020.

Project description

Project title: Coherent Control of Materials Properties
Main applicant: Oscar Grånäs, Division of Materials Theory​
Grant amount: 3 000 000 SEK for the period 2020-2023
Funder: Starting grant from the Swedish Research Council

Emergent phenomena, such as insulator to metal transitions, magnetism and superconductivity, often occur in materials where many degrees of freedom are strongly coupled to each other. To understand, and ultimately to control these phenomena is difficult, since the many intertwined effects makes it hard to infer causality relationships.

In this project we investigate how ultrafast laser-pulses can be used to perform coherent control of phase transitions in materials. The goal is to device a computationally tractable theory for optimizing the shape and frequency of laser pulses to minimize the energy and time needed to reach a certain transition, so called optimal control. We focus on low dimensional transition-metal di-chalcogenides, a class of materials know for many intriguing emergent properties, for example insulator to metal transitions.

We use a reformulation of quantum many-particle theory, time-dependent density-functional theory, that allows us to describe the coupling of external electromagnetic fields, electrons and ionic vibrations with a reduced number of variables. The resulting reduction of dimensionality makes optimal control of solids tractable with modern optimization algorithms.

The computational framework will be used to uncover how to access new states of these materials. In addition, a new classification framework for phase transitions is suggested, based on the causal sensitivity to optimal driving.

Last modified: 2023-03-27