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A Universal Approach to Solvation Modeling
Abstract
Continuum mean-field models that have been carefully designed to address the various electrostatic and nonelectrostatic interactions that develop between a molecule and a surrounding medium are particularly efficient tools for studying the effects of condensed phases on molecular structure, energetics, properties, spectra, interaction potentials, and dynamics. The SM8 model may be combined with density functional theory or Hartree-Fock theory to describe a solute's electronic structure and its self-consistent-field polarization by a solvent. A key feature is the use of class IV charge models to obtain accurate charge distributions (either in the vapor phase or in solution), even when using small basis sets that are affordable for large systems. A second key feature is that nonelectrostatic effects due to cavity formation, dispersion interactions, and changes in solvent structure are included in terms of empirical atomic surface tensions that depend on geometry but do not require atom-type assignments by the user. Use of an analytic surface area algorithm provides very stable energy gradients that allow geometry optimization in solution. The SM8 continuum model, the culmination of a series of SMx models (x = 1-8), permits the modeling of such diverse media as aqueous and organic solvents, soils, lipid bilayers, and air-water interfaces. In addition to predicting accurate transfer free energies between gaseous and condensed phases or between two different condensed phases, SMx models have been useful for predicting the significant influence of condensed phases on processes associated with a change in molecular charge, including acid/base equilibria and oxidation/reduction processes. In this Account, we provide an overview of the algorithms associated with the computation of free energies of solvation in the SM8 model. We also compare the accuracies of the SM8 model with those of other continuum solvation models. Finally, we highlight applications of the SM8 models to compute ionic solvation free energies, oxidation and reduction potentials, and pK(a) values.
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Hermann, R. B. (1972) · The Journal of Physical Chemistry · doi:10.1021/j100663a023 · Cited by 700
Cited by
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Supramolecular Binding Thermodynamics by Dispersion‐Corrected Density Functional Theory
Grimme, S. (2012) · Chemistry – A European Journal · doi:10.1002/chem.201200497 · Cited by 2,111
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Revised self-consistent continuum solvation in electronic-structure calculations
Andreussi, O., Dabo, I., & Marzari, N. (2012) · The Journal of Chemical Physics · doi:10.1063/1.3676407 · Cited by 538
Computational electrochemistry: prediction of liquid-phase reduction potentials
Marenich, A. V., Ho, J., Coote, M. L., Cramer, C. J., & Truhlar, D. G. (2014) · Phys. Chem. Chem. Phys. · doi:10.1039/c4cp01572j · Cited by 508
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Metadata
- Authors
- Cramer, C. J., & Truhlar, D. G.
- Venue
- Accounts of Chemical Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-05-31
- Volume
- 41
- Issue
- 6
- Pages
- 760-768
- ISSN
- 0001-4842 (ISSN-L)
- 1520-4898
- OpenAlex
- W1971209681
- Cited by
- 610
- References
- 1
- FWCI
- 19.34
- Type
- journal article
- Language
- en
- Open access
- closed
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