Electrochemical processes are the cornerstone of the energy transition sought for by many countries, especially in Europe. An important goal is the development of energy storage devices with better energy/power density and enhanced cycle lifes. Due to the huge experimental efforts made over the past decades, the list of available technologies (Li-ion, Na-ion, Li-metal, Li-S, supercapacitors, etc) has substantially expanded, and even more so has the list of potential materials. Computational approaches are essential to understand/rationalize/predict these materials' performances. Target properties include bulk materials structure, potentials, electronic & ionic conductions, etc. In addition, it is necessary to characterize the various interfaces that are formed between the electrodes and the electrolytes. 

The theoretical treatment of electrochemical systems is characterised, however, by a high level of complexity [1]. Non-trivial chemistry and electrodynamics are intertwined to form an intrinsically multiscale model, with subsystems that can be treated only at the electronic structure level, while others as a continuum. Classical models for the interactions, including polarization effects and a non-trivial treatment of the electrodes, also play a role. From a methodological perspective, although the modelling of bulk properties is now well established, many approaches have recently been developed for handling the much more complex case of the interfaces [2]. In the following we introduce some example of new methodological developments that have gained interest over the community:

On the one hand, grand-canonical density functional theory (GC-DFT) methods have been extensively used to simulate the quantum properties of electrochemical interfaces by keeping the metal electrode at a fixed electrochemical potential, while introducing the self-consistent interaction with the electrolyte solution through a polarizable continuum medium [3,4]. Because of their first-principles nature, these approaches have proven to be particularly suited for the simulation of electrochemical reactions and the rationalisation of absorption mechanisms [5].

On the other hand, constant-potential molecular dynamics (MD) methods have been used for the classical atomistic modelling of complete electrochemical cells under an applied voltage, paving the way to the accurate simulation of energy-storage devices [6,7]. In this context, recent advancements in classical density functional theories (c-DFT) are also acquiring considerable attention as a valuable strategy to compute accurate solvation free-energies of electrochemical interfaces and gain new insights on the thermodynamic stability of electrochemical products and reactants [8,9]. 

Furthermore, the modern theory of polarisation [10] and its application to deal with finite electric fields [11] or electric displacement fields [12] fostered recent advancements in the modelling of metal-electrolyte interactions, which open venues to introduce an explicit treatment of the electrolyte while maintaining a quantum-level description of the system [13]. On this front, equivariant and long-range machine-learning methods [14,15] hold great promise in overcoming the time and length scale limit associated with current first-principles approaches and predicting the non-local electronic response of the electrochemical interface under applied fields [15,16].

On top of these methods, data science approaches such as high throughput screening or the use of machine learning models have progressively gained momentum to handle the vast compositional space available for the materials [17]. For example, the development of Aidaa framework has opened the way towards the execution of automated workflows to screen materials for a specific application [18].

As described above, different simulation communities are very active in this area. One of the goals of this workshop is to enhance communication among them, and in particular between the materials science and the physical chemistry simulators.