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Abstract
This app empowers users to simulate ions confined between material surfaces that are nanometers apart, and extract the associated ionic structure. The app facilitates investigations for a wide array of ionic and environmental parameters using standard molecular dynamics (MD) method for unpolarizable surfaces.
- The code enables simulations of ions confined between nanoparticles (NPs) or other material surfaces
- Length of confinement is of the order of nanometers
- Materials represent nanoparticles (NPs) or biomacromolecules
- NP surfaces are treated as planar walls due to the large size difference between ions and NPs
- Users can extract the ionic structure (density profile) for a wide variety of ionic and environmental parameters
- Unpolarized surfaces are assumed and standard molecular dynamics is used to propagrate the dynamics of ions
Users can explore interesting effects by changing the c parameter from 0.3 to 0.9 M. This increase in density leads to crowding of the channel (confinement) with a large number of ions. The effect of symmetry breaking caused by the surfaces is seen: to avoid being pushed by ions from both the sides, an ion prefers the interface over the central region (bulk). The app enables users to explore this ion accumulation effect near the interface, and make a quantitative assessment of ionic structure in strong confinement.
Another rich avenue to explore is to tune the valency of positive ions (parameter z) from 1 to 3. A positively-charged multivalent ion (+3 Fe or +2 Ca) near an interface is pulled away from the interface by oppositely charged ions with a stronger force relative to the bulk where the symmetry allows for no preferred movement. Thus, stronger electrostatic interactions (as in the case of multivalent ions) tend to cause depletion of the ions from the interface. This app empowers users to investigate this depletion effect via accurate computation of the density profiles of ions.
Effects of changing other physical attributes such as confinement length and ion size, in various possible combinations, are also available for users to explore. We invite users to take an inside look at what happens to the self-assembly of ions in these nanoscale channels by investigating the interplay of electrostatic effects and steric (or entropic) effects caused due to confinement, and measuring associated density profiles.
Simulation of confined ions is performed using LAMMPS, and pre- and post-processing is carried out using the in-house C++ code.
For further details please refer to [https://softmaterialslab.github.io/nanoconfinement-md/]
Sponsored by
NSF award 1720625 (Network for Computational Nanotechnology - Engineered nanoBIO Node)
References
Jadhao, Vikram, and J. C. S. Kadupitiya. "Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning." 2020 IEEE/ACM Workshop on Education for High-Performance Computing (EduHPC). IEEE, 2020.
Kadupitiya, J. C. S., et al. "Machine learning surrogates for molecular dynamics simulations of soft materials." Journal of Computational Science 42 (2020): 101107.
Kadupitiya, J. C. S., Geoffrey C. Fox, and Vikram Jadhao. "Machine learning for performance enhancement of molecular dynamics simulations." International Conference on Computational Science. Springer, Cham, 2019.
Kadupitiya, J. C. S., Fox, G. C., & Jadhao, V. (2019, June). Machine learning for performance enhancement of molecular dynamics simulations. In International Conference on Computational Science (pp. 116-130). Springer, Cham.
S. Marru and V. Jadhao, “Development of the Nanoconfinement Science Gateway”, Gateways 2017, Ann Arbor, Michigan (2017)
Y. Jing, V. Jadhao, J. Zwanikken, and M. Olvera de la Cruz, "Ionic structure in fluids confined by planar dielectric interfaces”, J. Chem. Phys. 143, 194508 (2015)
V. Jadhao, F. J. Solis, and M. Olvera de la Cruz, “Simulation of charged systems in heterogeneous dielectric media via a true energy functional”, Phys. Rev. Lett. 109, 223905 (2012)
Cite this work
Researchers should cite this work as follows:
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Jadhao, Vikram, and J. C. S. Kadupitiya. "Integrating Machine Learning with HPC-driven Simulations for Enhanced Student Learning." 2020 IEEE/ACM Workshop on Education for High-Performance Computing (EduHPC). IEEE, 2020.
Kadupitiya, J. C. S., et al. "Machine learning surrogates for molecular dynamics simulations of soft materials." Journal of Computational Science 42 (2020): 101107.
Kadupitiya, J. C. S., Geoffrey C. Fox, and Vikram Jadhao. "Machine learning for performance enhancement of molecular dynamics simulations." International Conference on Computational Science. Springer, Cham, 2019.
Kadupitiya, Kadupitige, et al. "Ions in nanoconfinement." (2017).