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Computational screening of battery electrolyte components is an extremely challenging task because very complex features like solidelectrolyte-interphase (SEI) formation and graphite exfoliation need to be taken into account at least at the final screening stage. We present estimators for both SEI formation and graphite exfoliation based on a combinatorial approach with quantum chemistry calculations on model system reactions, which can be applied automatically for a large number of compounds and thus allows for a systematic first assessment of the relevant properties within screening approaches. Thermodynamic effects are assessed with quantum mechanical calculations, a more heuristic approach is used to estimate kinetic effects., Comment: Supplementary information available from martin.korth@uni-ulm.de

The performance of semi-empirical quantum mechanical (SQM), density functional theory (DFT) and wave function theory (WFT) methods is evaluated for the purpose of screening a large number of molecular structures with respect to their electrochemical stability to identify new battery electrolyte solvents. Starting from 100,000 database entries and based on more than 46,000 DFT calculations, 83 candidate molecules are identified and then used for benchmarking lower-level computational models (SQM, DFT) with respect to higher-level WFT reference data. A combination of SQM and WFT methods is suggested as a screening strategy at the electronic structure theory level. Using a subset of over 11,000 typical organic molecules and based on over 22,000 high-level WFT calculations, several simple models are tested for the prediction of ionization potentials (IPs) and electron affinities (EAs). Reference data are made available for the development of more sophisticated QSPR models.

The first-principles modeling of biomaterials has made tremendous advances over the last few years with the ongoing growth of computing power and impressive developments in the application of density functional theory (DFT) codes to large systems. One important step forward was the development of dispersion corrections for DFT methods, which account for the otherwise neglected dispersive van der Waals (vdW) interactions. Approaches at different levels of theory exist, with the most often used (semi-)empirical ones based on pair-wise interatomic C6R(-6) terms. Similar terms are now also used in connection with semiempirical QM (SQM) methods and density functional tight binding methods (SCC-DFTB). Their basic structure equals the attractive term in Lennard-Jones potentials, common to most force field approaches, but they usually use some type of cutoff function to make the mixing of the (long-range) dispersion term with the already existing (short-range) dispersion and exchange-repulsion effects from the electronic structure theory methods possible. All these dispersion approximations were found to perform accurately for smaller systems, but error estimates for larger systems are very rare and completely missing for really large biomolecules. We derive such estimates for the dispersion terms of DFT, SQM and MM methods using error statistics for smaller systems and dispersion contribution estimates for the PDBbind database of protein-ligand interactions. We find that dispersion terms will usually not be a limiting factor for reaching chemical accuracy, though some force fields and large ligand sizes are problematic.

Enthalpy–entropy compensation phenomena are of high importance for processes with dominating non-covalent interactions, like protein–ligand binding. We show here that non-covalent interactions are compensated by entropic effects not on an equal footing, but dependent on their electronic nature. This insight helps us to understand the observed diversity of enthalpy–entropy compensation phenomena and opens up a new route for the prediction of the entropic effects of non-covalent binding.