Graph-based approaches for predicting solvation energy in multiple solvents: open datasets and machine learning models
… from density functional theory (DFT) simulations using the SMD solvation model (19) and is
… ML prediction model of molecular solvation energies trained on this database. Our models …
… ML prediction model of molecular solvation energies trained on this database. Our models …
Prediction of solvation free energies of ionic solutes in neutral solvents
… The accuracy of predictions for solvation free energies of ionic … approaches for solvation
free energy prediction of several … COSMO-RS, cluster continuum model (CCM) together with …
free energy prediction of several … COSMO-RS, cluster continuum model (CCM) together with …
Quantitative Kinetic Model of CO2 Absorption in Aqueous Tertiary Amine Solvents
… implicit solvation model to obtain the solvation energy of molecular CO 2 while the solvation
… In summary, a quantitative model has been established for the prediction of absorption rates …
… In summary, a quantitative model has been established for the prediction of absorption rates …
Advances and challenges in modeling solvated reaction mechanisms for renewable fuels and chemicals
Y Basdogan, AM Maldonado… - Wiley Interdisciplinary …, 2020 - Wiley Online Library
… done in understanding their applicability for determining kinetic barriers. In the cases of … For
instance, mixed implicit/explicit solvation is used to predict energy calculations of ions and/or …
instance, mixed implicit/explicit solvation is used to predict energy calculations of ions and/or …
Group contribution and machine learning approaches to predict Abraham solute parameters, solvation free energy, and solvation enthalpy
… predictions than any one of them individually. Finally, we present our compiled solute parameter,
solvation energy, and solvation … access to our final prediction models through a simple …
solvation energy, and solvation … access to our final prediction models through a simple …
Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs
TN Borhani, S García-Muñoz, CV Luciani… - Physical Chemistry …, 2019 - pubs.rsc.org
… Due to the importance of the Gibbs free energy of solvation in understanding many … -phase
reaction equilibrium and kinetics, there is a need for predictive models that can be applied …
reaction equilibrium and kinetics, there is a need for predictive models that can be applied …
Kinetic solvent effects in organic reactions
BL Slakman, RH West - Journal of Physical Organic Chemistry, 2019 - Wiley Online Library
… prediction based on molecular properties, using models … needed for detailed kinetic modeling,
machine learning has … of kinetic models and the estimation of solvation thermochemistry. …
machine learning has … of kinetic models and the estimation of solvation thermochemistry. …
Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies
… model to reproduce high-quality experimental kinetic data for the nucleophilic aromatic
substitution reaction and use it to predict … in the DFT energies and the solvation model. Accurate …
substitution reaction and use it to predict … in the DFT energies and the solvation model. Accurate …
“Ion Solvation Spectra”: free energy analysis of solvation structures of multivalent cations in aprotic solvents
A Baskin, D Prendergast - The journal of physical chemistry letters, 2019 - ACS Publications
… kinetics, is closely connected to the ease of ion (de)solvation, … ab initio MD free energy
samplings (Figure 1e,f) predict two well-… As in the case of ACN, in THF, ab initio MD predicts the …
samplings (Figure 1e,f) predict two well-… As in the case of ACN, in THF, ab initio MD predicts the …
Modeling of Electron‐Transfer Kinetics in Magnesium Electrolytes: Influence of the Solvent on the Battery Performance
… As indicated by Figure 1 the kinetic model simplifies both the (de)solvation and the electron
… i is set to zero as soon as the model predicts negative values and corresponding results are …
… i is set to zero as soon as the model predicts negative values and corresponding results are …
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