Speaker
Description
Processes in electrochemical energy storage and conversion are critical to mitigate the detrimental effects of global warming. We will show that atomistic simulations based on quantum chemical methods together with machine-learning approaches can contribute to improve, e.g., the ion mobility and stability of battery materials and the catalytic activity of electrochemical interfaces.
The standard quantum chemical approach to reliably address materials properties is based on density functional theory. We are employing the Vienna ab initio simulation package (VASP). This code is so popular that it has been estimated that 10-20% of the workloads running on national supercomputing centers in the US and Europe are VASP-centric calculations. At the same time, typical DFT codes still show an unfavorable scaling with the number of nodes and cores due to the intrinsic non-locality of quantum mechanics.
Nowadays machine-learning interaction potentials (MLIPs) become more and more popular, as they combine a high numerical efficiency with near-DFT accuracy for the calculation of standard materials properties. However, typically they correspond to classical interaction potentials that do not take the electronic degrees of freedom into account. Consequently, they are still incapable of reliably reproducing properties that involve electronic properties. These include chemical reactions, optoelectronic properties of materials and charge equilibration, which are all relevant for a proper modelling of electrochemical energy storage and conversion. Hence there will still be a need for running reliable quantum chemical calculations to address electrochemical systems.
Most electrochemical devices involve the interface between an electron conductor, the electrode, and an ion conductor, the electrolyte. Electrodes typically consist of crystalline materials whose stability is often relative easy to determine from quantum-chemical total energy calculations. Even environmental conditions such as temperature, pressure and applied potential can often be effectively incorporated in a grand-canonical approach through the consideration of the corresponding chemical potentials. However, electrolytes are predominantly liquid which means that their proper description involves numerically demanding statistical averages. These are typically derived from computationally costly molecular dynamics simulations explicitly considering the atomic structure of the electrolyte. Recently approaches using so-called implicit solvent models in which the solvent is represented through a polarizable medium have become popular for the modelling of electrochemical interfaces. However, such a approach is in principle only reliable for non-polar solvents. This renders the description of liquid water problematic due to the strong polarity of the water molecules.
In spite of these obstacles, there has recently been substantial progress in the numerical modelling of electrochemical devices, as will be demonstrated in this contribution. It will be shown how simulations can contribute to an accelerated materials design for improved electrochemical devices through the employment of automated workflows. At the same time the still existing challenges in the modelling of electrochemical interfaces will be identified. To address these challenges does not only require improved numerical methods, but also a better conceptual understanding of the principles underlying electrochemical processes at interfaces.