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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/61989100:27740/25:10258825

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Chemical Science

ISSN

2041-6520

e-ISSN

2041-6539

Svazek periodika

16

Číslo periodika v rámci svazku

48

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

23129-23138

Kód UT WoS článku

001605862600001

EID výsledku v databázi Scopus

2-s2.0-105024936268