NYUAD Special Seminar: Mirza Galib
"Understanding simple chemistries in complex environments via molecular simulations"
Host: Alexej Jerschow
Zoom Link: https://nyu.zoom.us/j/95880001096
Abstract: Solvation and chemical reactions in complex environments play a vital role in many atmospheric and chemical processes. In addition to experiments, computational tools can significantly contribute to our fundamental understanding of these phenomena. However, the use of more accurate computational methods is always prohibited by the associated computational cost. Often time classical MD (molecular dynamics) are used due to its low computational cost. But in many complex environments these classical models are inadequate to describe the required solvation and reactions. Instead of classical force field, DFT (density functional theory) based ab initio MD (AIMD) and its machine learned prototype can be used as a more accurate alternative to study solvation and reactions in complex environments.
In this talk, I will first discuss how accurate is AIMD to describe ion solvation, and chemical reaction in bulk water. Specifically, I will discuss solvation of Na+, and dissociation reaction of H2CO3 in bulk water.[1,2] In the second part of the talk, I will show how the modern machine learning techniques can be used to extend the time scale and length scale of standard AIMD simulations.[3] Specifically, I will discuss the development of a reactive force field to describe reactions of N2O5 (an atmospherically important small gas) in water and at the air-water interface.[4] From the unprecedented molecular insight learned through that newly developed machine learned model, I will show how a liquid-vapour interface can modulate aqueous chemistry.[4] We found that the facile charge separation of N2O5 occurs via interfacial hydrolysis which leads to its reactive uptake into aqueous atmospheric aerosols and thereby creates an irreversible sink of NOx compounds in the nighttime air. Finally, I will discuss some ongoing work on extending the development of machine learned models for rare earth nickelates, a promising material for energy efficient computing.
1. Galib, Mirza et al. "Revisiting the hydration structure of aqueous Na+." The Journal of chemical physics 146, 8 (2017): 084504.
2. Galib, Mirza and Hanna, Gabriel "Mechanistic insights into the dissociation and decomposition of carbonic acid in water via the hydroxide route: An ab initio metadynamics study." The Journal of Physical Chemistry B 115, 50 (2011): 15024-15035.
3. Zhang, Linfeng et al., Phys. Rev. Lett., 120 (2018): 143001.
4. Galib, Mirza and Limmer, David "Reactive uptake of N2O5 by atmospheric aerosol is dominated by interfacial processes." Science 371, 6532 (2021): 921-925.