Scope

Phase diagrams are of ubiquitous importance for materials design. Current materials design workflows in industry and academia employ CALPHAD-computed phase diagrams that to a large extent rely on assessed experimental data.

Today the computation of large numbers of DFT data are becoming a routine task, due to efficient DFT codes, efficient workflow management and powerful high-performance computing. Together with progress in interatomic potentials, in particular the development of machine learning potentials as well as efficient implementations and parameterization codes, this means that interatomic potentials with near-DFT accuracy are now available. When combined with efficient sampling for the computation of free energies, it is therefore possible to estimate phase diagrams directly from DFT data and to supplement and assess experimental input.

At the three-day workshop we will provide tutorials and hands-on classes that cover the complete chain from high-throughput electronic structure calculations to the computation of phase diagrams. Day 1 will focus on automated workflows for the generation of DFT data. On day 2 we will discuss the parameterization and validation of interatomic potentials from DFT reference data. Day 3 will then introduce the methods and tools for the computation of thermodynamic properties and phase diagrams.

Onsite and online

The workshop will be run in hybrid mode, onsite and online participation is possible.