This is a renowned biennial workshop about recent progress in electronic structure methods and their applications. It is one of the most prominent international conferences in its field, filling the gap between the large-scale Psi-k conference and small-scale workshops focused on one approach or a single class of systems. The aim is a critical discussion of methods and challenging applications.  

The “Total Energy” workshop has been held since 1984, making it one of the longest running, established events in the scientific calendar. Since 1987 it is held at ICTP in Trieste every two years; in the even years a smaller workshop is held at different locations worldwide (e.g. in 2018 in Cambridge UK, in 2020 in San Sebastián Spain, in 2024 it is expected to be in Shanghai, China). As such, it has become one of the most popular, regular events of the international electronic-structure community, with a two-pronged focus on recent progress in electronic-structure methods, and in their applications to a steadily increasing range of complex materials and systems.

Widespread approaches used in the electronic structure community include DFT, TDDFT, GW-BSE, DMFT, and quantum Monte Carlo methods, which provide the foundation for computing many physical and chemical properties of solids, interfaces, liquids, and low-dimensional systems. There are numerous challenging applications for which the approximations used in these formalisms, or their technical limitations, do not yet permit accurate quantitative predictions of some properties of materials or electronic phenomena. The goal of the workshop is to provide a broad view of recent developments, current challenges, and future directions of the field. This is achieved by giving an in-depth analysis of a selection of scientific cases, and applications to pressing problems in condensed matter physics, chemistry, materials science, and associated engineering and scientific disciplines. Moreover, it will be a forum where approaches that have shown some promise more recently, such as DMRG, high-throughput workflows, or machine learning are explored, and where possibilities to combine different methods are discussed.