High-throughput spectroscopy of quantum point defects

PhD n°7


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 Denmark within the last 3 years are not eligible (unless as part of a procedure for obtaining refugee status under the Geneva Convention).


To develop an efficient theoretical framework for calculating the absorption and photoluminescence (PL) spectrum of point defects in insulating crystals and use it to perform a high- throughput characterisation of 1000 crystal/defect systems with the aim of identifying candidates for quantum technology applications (qubits, magnetic field sensing, and single photon sources). Since most applications of point defects in quantum technology takes advantage of the electron spin, we shall be most interested in defects that exhibit magnetic ground state and feature long spin coherence times. We will attempt to build machine learning models to predict key spectroscopic properties from simple chemical/structural features.

The specific objectives are:

  • Generate a computational database with relaxed structures for 1000 crystal point defects
  • Identify defects with a high-spin ground state and small reorganisation energies
  • Calculate the absorption and PL spectrum for the lowest optically active transition in the defects selected in the previous step
  • Train a machine learning model on the database to predict PL spectra and magnetic states using simple structural fingerprints

Expected Results:

  • A computational workflow for characterisation of the atomic and electronic structure of point defects
  • A database with structural, energetic, and spectroscopic properties of crystal point defects
  • A set of specific crystal/defect candidate systems with optimal properties for application as single-photon sources and solid-state qubits, respectively.

Planned secondment(s): 6 months

  • Intersectoral: SCM, Prediction of PL spectra of point defects using machine learning techniques developed for molecules. M18-20.
  • Academic: MLU, Prediction of PL spectra of point defects using machine learning techniques developed for interfaces. M24-26.

Enrolment in Doctoral degree(s):

Technical University of Denmark


Kristian Thygesen

PhD n°: PhD n°7
Country: Denmark
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