Crystallization Collective Variables
(123 Words, 1 Minutes)
Crystal nucleation is a challenging problem to study computationally due to its characteristic length and time scale. We employ enhanced sampling methods that push the system toward nucleation by enhancing the probability of observing transitions along a reaction coordinate, taking the name of Collective Variable (CVs). An interesting and important research question is, how can we best identify and compute such a crystallization CV? CVs are typically expressed as mathematical functions approximating the system’s degree of order/crystallinity. Such functions needs to be able to deal with all the complexities inherent to the nucleation problem at hand while, at the same time, being as computationally efficient as possible. Finding this compromise is challenging and an ongoing area of research in this group.
Relevant Publications:
- Insight into the nucleation of urea crystals from the melt, F Giberti, M Salvalaglio, M Mazzotti, M Parrinello, Chemical Engineering Science 121, 51-59, 2014.
- Analyzing and driving cluster formation in atomistic simulations, GA Tribello, F Giberti, GC Sosso, M Salvalaglio, M Parrinello, Journal of chemical theory and computation 13 (3), 1317-1327 2017.
- CO2 packing polymorphism under pressure: Mechanism and thermodynamics of the I-III polymorphic transition, I Gimondi, M Salvalaglio The Journal of chemical physics 147 (11), 2017.
- CO2 packing polymorphism under confinement in cylindrical nanopores I Gimondi, M Salvalaglio Molecular Systems Design & Engineering 3 (1), 243-252, 2018.
- A variational approach to assess reaction coordinates for two-step crystallization AR Finney, M Salvalaglio The Journal of Chemical Physics 158 (9), 2023.
- Machine Learning Nucleation Collective Variables with Graph Neural Networks, FM Dietrich, XR Advincula, G Gobbo, MA Bellucci, M Salvalaglio Journal of Chemical Theory and Computation, 2023.
Review: