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Estimation of Solvation Quantities from Experimental Thermodynamic Data: Development of the Comprehensive CompSol Databank for Pure and Mixed Solutes

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TLDR
The CompSol database as mentioned in this paper provides a large set of experimental solvation chemical-potential, solvation entropy, and solvation enthalpy data of pure and mixed components, covering extended temperature ranges.
Abstract
The Gibbs energy of solvation measures the affinity of a solute for its solvent and is thus a key property for the selection of an appropriate solvent for a chemical synthesis or a separation process. More fundamentally, Gibbs energies of solvation are choice data for developing and benchmarking molecular models predicting solvation effects. The Comprehensive Solvation-CompSol-database was developed with the ambition to propose very large sets of new experimental solvation chemical-potential, solvation entropy, and solvation enthalpy data of pure and mixed components, covering extended temperature ranges. For mixed compounds, the solvation quantities were generated in infinite-dilution conditions by combining experimental values of pure-component and binary-mixture thermodynamic properties. Three types of binary-mixture properties were considered: partition coefficients, activity coefficients at infinite dilution, and Henry's-law constants. A rigorous methodology was implemented with the aim to select data at appropriate conditions of temperature, pressure, and concentration for the estimation of solvation data. Finally, our comprehensive CompSol database contains 21 671 data associated with 1969 pure species and 70 062 data associated with 14 102 binary mixtures (including 760 solvation data related to the ionic-liquid class of solvents). On the basis of the very large amount of experimental data contained in the CompSol database , it is finally discussed how solvation energies are influenced by hydrogen-bonding association effects.

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Atomistic modeling of electrocatalysis: Are we there yet?

TL;DR: The joint theoretical and experimental efforts to design non‐noble hydrogen evolution catalysts are discussed as an example for the success of theory to spur and accelerate experimental discoveries.
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Transfer learning for solvation free energies: From quantum chemistry to experiments

TL;DR: In this paper, a transfer learning approach was proposed for the prediction of solvation free energies that combines fundamentals from quantum calculations with the higher accuracy of experimental measurements using two new databases CombiSolv-QM and CombiSv-Exp.
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References
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Journal ArticleDOI

Conductor-like Screening Model for Real Solvents: A New Approach to the Quantitative Calculation of Solvation Phenomena

TL;DR: In this paper, a new approach for the calculation of solvation phenomena is presented, based on the perfect, Le., conductor-like, screening of the solute molecule and a quantitative calculation of the deviations from ideality appearing in real solvents.
Journal ArticleDOI

Refinement and Parametrization of COSMO-RS

TL;DR: In this paper, the authors parametrized the COSMO and its extension beyond the dielectric approximation (COSMO-RS) in order to optimally reproduce 642 data points for a variety of properties.
Journal ArticleDOI

The Many Roles of Computation in Drug Discovery

TL;DR: An overview is given on the diverse uses of computational chemistry in drug discovery, with particular emphasis on virtual screening, de novo design, evaluation of drug-likeness, and advanced methods for determining protein-ligand binding.
Journal ArticleDOI

Determination of sets of solute descriptors from chromatographic measurements

TL;DR: It is concluded that for processes that entail transfer of a solute from one phase to another, only a small number of solute descriptors is needed to provide a reasonably accurate analysis of the process.
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