QuantumLaP: Predictive tools for laser processing
  • Latest news
  • Prediction tools
    • Quantum computations of materials excitation
      • Octopus sources
      • QuantumLaP repository
      • Photo-excitation database
      • [Octopus] Todo list
    • Plasmonic toolkit (Python/Windows/Linux)
      • Theory
      • Download the code
      • Experimental multi-material LIPSS database
      • Code documentation
      • [SPP-ext-theory] TODO list
    • Macroscopic Silicon predictive simulation tool for flat samples (C/C++, 1D/2D)
      • Link to Code Repository
      • Related publication list
      • Todo list
    • Macroscopic simulation of structured nanomaterials (Fortran, 2D/3D)
      • Wiki of the “FLAPS” code
      • Link to Code Repository
      • [Flaps] TODO list
  • Publications
    • Publications and patents
    • Press releases
    • Conferences and workshops
    • Outreach activities
    • List of all publications (out of QuantumLaP project)
  • Projects
    • MSCA-IF-2015-“QuantumLaP”
      • Project report summary (EU|CORDIS)
      • Timeline [priv.]
      • WP1 [priv.]
      • WP2 [priv.]
      • WP3 [priv.]
      • WP4 [priv.]
    • EU-MSCA-RISE-2018 “Atlantic” (public)
      • Public information
      • Detailed Consortium
      • Administration link (private)
    • EU-H2020-ERC-StG-2018 [priv.]
  • Contact us
    • Research Program 4 – Prof. Bulgakova
    • Theory Department (Prof. A. Rubio)
    • HiLASE (Prague)
    • Max Planck Institute for Structure and Dynamics of Matter (Hamburg)

Project tracking

Publications and patents

See the dedicated page

Conferences and workshops

See the dedicated page

Outreach activities

See the dedicated page

Awards

  1. July 2016: Venice School of Laser Ablation: 2nd prize in “Rising Stars” category.

Deliverables

  1. March 2017, Oral presentation to HiLASE center, 45 min. “Development of predictive tools for the laser tailoring of  material properties”, PDF abstract

Implementation

  • Project start: 1st of September 2015.
  • Project end: 31st of August 2017.

Funding

This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Actions (2015-2017) under grant agreement No 657424.

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