Solvation strategies for free-energy calculations in a halogen-bonded complex: implicit, explicit, and machine learning approaches

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388963%3A_____%2F25%3A00643383" target="_blank" >RIV/61388963:_____/25:00643383 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/25:10258825
Result on the web
<a href="https://doi.org/10.1039/D5SC06336A" target="_blank" >https://doi.org/10.1039/D5SC06336A</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1039/d5sc06336a" target="_blank" >10.1039/d5sc06336a</a>

Result language
angličtina
Original language name
Solvation strategies for free-energy calculations in a halogen-bonded complex: implicit, explicit, and machine learning approaches
Original language description
In pursuit of an efficient solvation approach for the halogen bonded complex between molecular iodine and tetramethylthiourea that reliably reproduces experimental trends, we investigated a range of solvent models, from implicit representations to periodic metadynamics simulations alongside micro-solvation and ONIOM-based methods as robust alternatives. Implicit solvent models fail to describe halogen-bonded complexes in high-polar solvents but provide surprisingly accurate estimates of binding free energies in all low to moderately polar solvents. For accurate and reliable modeling, especially in polar media, explicit solvent representations are essential. Periodic metadynamics simulations typically provide enhanced accuracy in calculating free energy differences, particularly for systems with complex solvation behavior. However, they are computationally demanding and restricted to generalized gradient approximation functionals (GGA). To overcome this limitation and improve accuracy, we employed the machine learning perturbation theory technique, enabling the estimation of free energies at levels of theory beyond the GGA.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10403 - Physical chemistry

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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