PhD n°11
Eligibility:
EU mobility rules apply. In principle, applicants can have any nationality and any current residence (although immigration rules apply, favoring
EU applicants). Candidates who have already been awarded a PhD degree are not eligible. In addition, candidates who have already spent more than 12 months in the Switzerland within the last 3 years are not eligible (unless as part of a procedure for obtaining refugee status under the Geneva Convention).
Objectives:
This project investigates the influence of adsorption geometry on magnetic interactions of porphyrin-based single-ion magnetic molecules with the underlying substrate. X-ray magnetic dichroism measurements show clear differences in magnetic exchange coupling including a cross-over from ferromagnetic to antiferromagnetic order in different adsorption configurations. Precise and reliable experimental data on the adsorption geometry (adsorption height, in particular) are, however, lacking due to the complexity of molecula systems. In this project, synchrotron-based angle- and energy-scanned photoelectron diffraction in connection with multiple-scattering calculations enhanced by machine-learning techniques will be used as a sensitive probe of the local structure around the magnetic centre of the molecule. A major part of the project shall be the development of efficient structural optimization code that will be able to handle the complexity of adsorbed organic molecules.
This will be done by complementing existing optimization code with scattering code developed in WP1 and machine learning techniques developed in WP2 of the network.
Key features of the new code:
- Make structural optimization more efficient by using machine-learning techniques to quickly
categorize a parameter vector as compatible or incompatible with measurement. - Reduce the number of parameter dimensions by automatically identifying and separating
significant parameters. - Interface with first-principles codes to (1) import electronic potentials for calculating scattering
factors and (2) verify specific models for compatibility with theory.
Expected Results:
- An uncomplicated, efficient and well-documented data analysis workflow for solving complex surface structures by leveraging machine-learning techniques and first-principles theory developed in the EuSpecLab network for the simulation of photoelectron diffraction patterns.
- Experimental determination of the adsorption geometry of magnetic porphyrins on magnetic surfaces and their influence on magnetic interaction strength and cross-over phenomena.
Planned secondment(s): 6 months
- Academic: CNRS: D. Sébilleau; M12-14; Training on the basic photoelectron diffraction code
- Intersectoral: PINFLOW: Jiri Varna M22-M24; modifications of surface of the electrodes studied
by PED and XPS
Enrolment in Doctoral degree(s):
University of Basel
Contact:
Matthias Muntwiller
matthias.muntwiler@psi.ch