Title: Virtual hands-on tutorial on ab-initio modeling of alloys using GreenALM

From Oct. 11- 14, 2021, the virtual Psi-k GreenALM hands-on tutorial 2021 is taking place. This tutorial is dedicated to ab-initio calculations of alloy properties using the GreenALM code, a modern Green’s function based DFT code that is specially efficient for the calculation of alloys, including random multicomponent alloys and paramagnetic systems.

The tutorial combines lectures by outstanding scientists in the fields of density functional theory (DFT), alloy theory and materials simulation with introductions to the features of the GreenALM code and hands-on sessions about calculations of alloy properties.

Free registration is open now: GreenALM Tutorial registration

List of speakers (with preliminary titles)

• Claudia Draxl, Humboldt-Universität Berlin(Basics of DFT)
• Blazej Grabowski, University of Stuttgart(Alloy thermodynamics)
• Sergei Simak, Linköping University(Structural transitions)
• Pasha Korzhavyi, KTH Stockholm(Mechanical properties + defects from ab-initio)
• Francesco Maresca, University of Groningen(Multi-scale simulation of mechanical properties and solid-solution strengthening)
• Julie Staunton, University of Warwick(Green's function DFT for alloys)
• Andrei Ruban, KTH Stockholmand MCL (Thermodynamics of paramagnetic multicomponent alloys with GreenALM)
• Oleg Peil, MCL(Accelerated alloy design based on machine learning and DFT)
• Lorenz Romaner, Montanuniversität Leoben(Reassessment of CALPHAD databases combining experiment and DFT)

Topics of hands-on tutorials

1. Electronic structure calculations: Green's function DFT, SCF cycle, convergence tests, equation of state, band structure, Fermi surface.
2. Alloy calculations with GreenALM: CPA, electronic properties, formation energy.
3. Advanced alloy properties and simple defects with GreenALM.
4. Phase stability with GreenALM.
5. Thermodynamic properties with GreenALM.
6. Solid-solution strengthening workflow based on GreenALM.
7. Accelerated alloy design based on machine learning and DFT.

Best regards,

Oleg Peil, Andrei Ruban, Lorenz Romaner and Jürgen Spitaler