The Lithium–Thiophene Riddle Revisited

Korth, Martin; Towler, M. D.

doi:10.1021/jp204132g

Cited by 21 publications

(40 citation statements)

References 33 publications

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“…36 The results presented here are thus based on simplified model systems and approximate computational methods, and should be used with appropriate care (as simpler problems were shown to sometimes require much more advanced methods). 37 Estimating critical complex properties: SEI composition An important goal for research into advanced electrolyte systems would be the identification of criteria for forming functional SEI films, based on the molecular composition of electrolyte systems. On the experimental side, several combined studies on battery performance (an indicator for functional SEI film) and SEI composition were published in the past (for an overview see ref.…”

Section: Methodological Detailsmentioning

confidence: 99%

How to estimate solid-electrolyte-interphase features when screening electrolyte materials

Husch

Korth

2015

Phys. Chem. Chem. Phys.

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Computational screening of battery electrolyte components is an extremely challenging task because very complex features like solid-electrolyte-interphase (SEI) formation and graphite exfoliation need to be taken into account at least in the final screening stage. We present estimators for both SEI formation and graphite exfoliation based on a combinatorial approach using quantum chemistry calculations on model system reactions, which can be applied automatically for a large number of compounds and thus allows for the systematic first assessment of the relevant properties using screening approaches. The thermodynamic effects are assessed using quantum mechanical calculations, while a more heuristic approach is used to estimate the kinetic effects.

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Section: Methodological Detailsmentioning

confidence: 99%

How to estimate solid-electrolyte-interphase features when screening electrolyte materials

Husch

Korth

2015

Phys. Chem. Chem. Phys.

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“…It is known from the literature that biomolecular interactions are guided by non-covalent forces, which require very high-level methods to be accurately described without empirical input. 73,74 As the \gold standard" method of Quantum Chemistry, CCSD(T) (i.e. coupled-cluster theory with single and double excitations and perturbative triples) with large basis sets, is not applicable to large systems in a black box way outside developers labs, people commonly turn to extrapolation schemes to produce highly-accurate reference values.…”

Section: Computational Detailsmentioning

confidence: 99%

Benchmark of electronic structure methods for protein–ligand interactions based on high-level reference data

Yilmazer

Heitel

Schwabe

et al. 2015

J. Theor. Comput. Chem.

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The accurate prediction of the strength of protein-ligand interactions is a very di±cult problem despite impressive advances in the¯eld of biomolecular modeling. There are good reasons to believe that quantum mechanical methods can help with this task, but the application of such methods in the context of scoring is still in its infancy. Here we benchmark several wave function theory (WFT), density functional theory (DFT) and semiempirical quantum mechanical (SQM) approaches against high-level theoretical references for realistic test cases. Based on our ndings for systematically generated model systems of real protein/ligand complexes from the PDB-bind database, we can recommend SCS-MP2 and B2-PLYP-D3 as reference methods, TPSS-D3+D abc /def-TZVPP as the best DFT approach and PM6-DH+ as a fast and accurate alternative to full ab initio treatments.

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“…26 There is on the other hand no approach available to compute the operative voltage of real systems without ad-hoc adjustments, and even the basic interactions of lithium atoms with organic molecules are nontrivial if very high accuracy is needed. 56 This is one of the reasons why we do not focus on the agreement of the actual values with experiment, but on correctly ranking compounds with respect to their stability. Also in the following, we find that our ranking predictions are fitting if ideal conditions apply, but not in the case of real systems (see below for details).…”

Section: Introductionmentioning

confidence: 99%

Toward New Solvents for EDLCs: From Computational Screening to Electrochemical Validation

Schütter¹,

Husch

Korth

et al. 2015

J. Phys. Chem. C

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The development of innovative electrolytes is a key aspect of improving electrochemical double layer capacitors (EDLCs). New solvents, new conducting salts as well as new ionic liquids need to be considered. To avoid time-consuming "trial and error" experiments, it is desirable to "rationalize" this search for new materials. An important step in this direction is the systematic application of computational screening approaches. Via the fast prediction of the properties of a large number of compounds, for instance all reasonable candidates within a given compound class, such approaches should allow to identify of the most promising candidates for subsequent experiments. In this work we consider the toy system of all reasonable nitrile solvents up to 12 heavy atoms. To investigate if our recently proposed computational screening strategy is a feasible tool for the purpose of rationalizing the search for new EDLC electrolyte materials, we correlatein the case of EDLCs for the first timecomputational screening results with experimental findings. For this, experiments are performed on those compounds for which experimental data is not available from the literature. We find that our screening approach is well suited to pick good candidates out of the set of all reasonable nitriles, comprising almost 5000 compounds.

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The Lithium–Thiophene Riddle Revisited

Cited by 21 publications

References 33 publications

How to estimate solid-electrolyte-interphase features when screening electrolyte materials

How to estimate solid-electrolyte-interphase features when screening electrolyte materials

Benchmark of electronic structure methods for protein–ligand interactions based on high-level reference data

Toward New Solvents for EDLCs: From Computational Screening to Electrochemical Validation

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