Exploring, understanding, and describing materials with strong electronic Coulomb correlations remain among the big challenges of modern condensed matter physics. Correlated materials are characterized by an extreme sensitivity to external probes such as pressure or temperature, and slight changes in composition, constraints during the growth process (e.g. by heterostructuring) or off-stoechiometries can significantly alter their properties. As a result, even a qualitatively reliable theoretical description may depend on quantitative details of the electronic structure, stressing the need for both, an accurate treatment of many-body effects and first principles techniques with predictive capabilities. The task is even harder as, in general, the properties of correlated materials display intrinsic non-trivial temperature dependences (in particular beyond simple Fermi factors) and many of the most intriguing phenomena involve excited states.
Despite of these challenges, the field is rapidly evolving, and progress at the many-body theory and first principles frontiers, as well as at their intersection, is substantial. Moreover, recent developments in machine learning and data science promise to greatly extend the scope of many-body methods and improve our ability to discover new correlated electron physics.
The symposium will address recent advances in
1) numerical many-body techniques
2) interfacing many-body techniques with electronic structure theory
3) improved electronic structure schemes for both, ground and excited states
4) the description of particularly challenging materials classes
5) algorithmic/machine learning aspects
Please note that while the invited lectures will have a focus on correlated electron materials, the symposium will cover the general field of computational materials and electronic structure theory.