We are looking for a PhD student, who is interested in code development and application of the developed methods to next-generation quantum materials. The application deadline is February 03, 2023. More information on the position and instructions how to apply can be found https://psi-k.net/jobs/2-phd-positions-at-tu-dresden-in-application-and-d.
Author Archives: dgolze
Welcome Moritz
Moritz just joined our group as PhD student.
Two new papers about GW and BSE@GW method development published in JCTC
We just published a paper about “Benchmark of GW Methods for Core-Level Binding Energies” https://pubs.acs.org/doi/10.1021/acs.jctc.2c00617 and another one about “Combining Renormalized Singles GW Methods with the Bethe–Salpeter Equation for Accurate Neutral Excitation Energies” https://pubs.acs.org/doi/10.1021/acs.jctc.2c00686 with our collaborators from Duke University (USA).
Our XPS prediction model combining DFT+GW+ML is now out in Chemistry of Materials
Check out our paper at https://pubs.acs.org/doi/10.1021/acs.chemmater.1c04279. For the brave, try our XPS prediction server at http://nanocarbon.fi/xps to obtain a prediction of carbon-based materials within seconds. A short story is also available here: https://miguelcaro.org/wp/2022/07/13/automated-x-ray-photoelectron-spectroscopy-xps-prediction-for-carbon-based-materials-combining-dft-gw-and-machine-learning/
PhD position available
We are looking for a PhD student, who is interested in code development; more precisely in the development of low-scaling electronic structure methods. The application deadline is May 31, 2022. More information on the position and instructions how to apply can be found here.
Publication on computation of molecular XAS spectra from BSE@GW
Check out our paper in JCTC “All-Electron BSE@GW Method for K-Edge Core Electron Excitation Energies”, where we showed that absolute core-level transition energies from BSE@GW are in excellent agreement with experiment, with a mean average error of only 0.63 eV.
ML predictions of core-level binding energies of carbon-based materials
Check out our latest work on machine learning models for computational predictions of core-electron binding energies of carbon-based materials: Accurate computational prediction of core-electron binding energies in carbon-based materials: A machine-learning model combining DFT and GW (arXiv:2112.06551)
TUD Young Investigator
D. Golze received the TUD Young Investigator status.