Crystal polymorphism of complex liquids plays a crucial role in industrial crystallization, food technology, pharmaceuticals, and materials engineering. However, the experimental identification of unknown crystal structures can be challenging, particularly for high-viscosity complex liquids, such as ionic liquids (ILs). In this study, we performed a molecular dynamics simulation coupled with metadynamics to investigate an imidazolium IL (1-alkyl-3-methylimidazolium hexafluorophosphates). The simulation employed two distinct radial-distribution functions, represented by Gaussian window functions as collective variables, and revealed at least two crystal-like phases distinct from the known α and β crystal phases typically formed by this IL. Additionally, the simulation unveiled a unique phase characterized by the ordered spatial arrangement of anion aggregations. These crystal-like and unique phases emerged regardless of the potential used. The simulation methodology presented here is broadly applicable for exploring unknown phases in complex systems and contributes to the design of functional materials, such as porous ILs for gas molecule capture and separation.
Skip Nav Destination
Article navigation
28 May 2024
Research Article|
May 22 2024
Unknown crystal-like phases formed in an imidazolium ionic liquid: A metadynamics simulation study
Special Collection:
Porous Solids for Energy Applications
Hiroki Nada
Hiroki Nada
a)
(Investigation, Methodology, Writing – original draft)
Graduate School of Engineering, Tottori University
, 4-101 Koyama-Minami, Tottori 680-8552, Japan
a)Author to whom correspondence should be addressed: hnada@tottori-u.ac.jp
Search for other works by this author on:
a)Author to whom correspondence should be addressed: hnada@tottori-u.ac.jp
J. Chem. Phys. 160, 204501 (2024)
Article history
Received:
February 28 2024
Accepted:
May 07 2024
Citation
Hiroki Nada; Unknown crystal-like phases formed in an imidazolium ionic liquid: A metadynamics simulation study. J. Chem. Phys. 28 May 2024; 160 (20): 204501. https://doi.org/10.1063/5.0206020
Download citation file:
Sign in
Don't already have an account? Register
Sign In
You could not be signed in. Please check your credentials and make sure you have an active account and try again.
Pay-Per-View Access
$40.00
310
Views
Citing articles via
DeePMD-kit v2: A software package for deep potential models
Jinzhe Zeng, Duo Zhang, et al.