ISTPC is an international two-week summer school in electronic structure theory whose first edition was held in June 2015 (in Aussois, France). Since then, it has been organized three times in person (in 2017, 2022, and 2024) and once online (in 2021, because of the covid). The main topic of the school is electron correlation in physics and chemistry, with a strong emphasis on the theoretical foundations of standard electronic structure methods. Emerging approaches are also introduced. The many-electron problem is the common denominator to all the lectures. It will be described, for ground and excited states, both in molecular and extended systems, with a multiple set of concepts that come from both the quantum chemistry and the solid-state physics communities. Both model and ab initio approaches will be covered. 

The strongly multidisciplinary program of the school, which makes ISTPC unique, echoes recent methodological efforts that often take place at the frontier between quantum chemistry and condensed matter physics, but also mathematics and, more recently, machine learning and quantum computing. We can mention, for example, density functional theory (DFT) [1], its extension to excited states [2-7] and combination with many-electron wave functions [8-11]; one-electron reduced density matrix functional theory used as a reference frame from which, for example, the description of charged electronic excitations can be improved [12,13,47,48]; density matrix quantum embedding theory (whose original formulation [14] exploits tools from matrix product state theory [50]), its mathematical construction [15-18] and formal connection to DFT [19-21]; Green's function methods [42-46] and their implementation in quantum chemistry [22-26,51-55]; Wave function theory of electronic excitations [56-58], with a particular focus on orbital optimization [27,28,49]; the development of machine learning techniques for quantum chemistry [39,40] and materials science [37,38,41]; and, finally, the adaptation of electronic structure methods to neural networks [36] and quantum algorithmics [29-35].

One of the main motivations for teaching formal aspects of electronic-structure theory is to highlight and explain both strengths and limitations of widely used computational methods in quantum chemistry and condensed matter physics. Another important aspect is the exploration of new theories and concepts that have been developed recently or are still under development, in particular for the description of strongly correlated molecular and extended systems. For such systems, the standard paradigm of solid-state physics consisting in describing interacting electrons as a collection of weakly interacting (quasi-)particles breaks down and new methods are needed. Interestingly, these methods often mix concepts that come from either solid-state physics or chemistry. In addition, attention should also be paid to the recent and fast development of machine learning techniques and quantum computing in the modeling of (strongly correlated) electronic structures. 

On the basis of these observations, we established the following (highly multidisciplinary) list of topics that will be covered during the school.

Quantum Chemistry (basic and advanced courses):
Hartree-Fock theory, Post-Hartree-Fock methods, Multi-Configurational Self-Consistent Field, Multi-reference perturbation theory. 
Condensed matter physics (basic and advanced courses):
Solid-state physics (introduction), Green's functions, GW method, Bethe-Salpeter equation, Dynamical mean-field theory.    
At the frontier between quantum chemistry and condensed matter physics (basic and advanced courses):   
Second quantization, mathematical aspects of electronic structure theory, density-functional theory (DFT), time-dependent DFT, Green's function methods for Quantum Chemistry, Stochastic approaches.
Recently established or emerging approaches:
Density matrix renormalization group method and matrix product states, density matrix embedding theory, neural network quantum states, quantum computing of electronic wavefunctions and Green’s functions, machine learning of materials science and quantum chemistry.  

Registration and fee: Updates about the registration process will be provided on the website of the school at the beginning of 2026. Information about the previous editions can also be found on the website. Registration will be confirmed once the participants have paid the registration fee (about 1200€), which covers both accommodation (in a double room) and food expenses for the two weeks of the school. See https://lcqs.unistra.fr/istpc-2026/ for further information.