Introducing CD-WAC: core-level GW calculations just got faster

We are pleased to announce the publication of our recent paper, “Accelerating core-level GW calculations by combining the contour deformation approach with the analytic continuation of W”. This novel work introduces the CD-WAC method, combining contour deformation (CD) with an analytic continuation of the screened Coulomb interaction W, effectively reducing the scaling of CD fromContinue reading “Introducing CD-WAC: core-level GW calculations just got faster”

Participation at the Helmholtz GPU Hackathon

Our entire group, recently participated in the Helmholtz GPU Hackathon held at Forschungszentrum Jülich. Our main objective was to offload the demanding computational tasks performed by the Resolution of Identity (RI) routines in FHIaims to GPUs using CUDA. We not only succeeded in implementing GPU support within the RI routines, but we also had anContinue reading “Participation at the Helmholtz GPU Hackathon”

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/

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)