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Special Seminar
 
Name: Dr. Sohan Kundu
Affiliation: Postdoctoral Research Scientist, Columbia University
 Title: Quantum and Classical Dynamics of Energy Transfer and Storage Systems: From Photosynthesis to Lithium Batteries
Date & Time: Monday, 21st October at 11.30 a.m.
Venue: Rajarshi Bhattacharya Memorial Lecture Hall, Chemical Sciences Building
Abstract:
In this talk I will discuss recent work studying the impact of coupled electron-nuclear motion on chemical processes relevant for energy transfer and storage. The talk consists of two sections: In Section I, I will explore how electron-vibration couplings inEluence the dynamics of intermolecular excitation energy transfer (EET), which has key bearings on our understanding of photosynthetic light harvesting, and on the design of energy-efEicient materials. I will mainly recount the development [1,2] and use [3] of real time path integral methods to perform the Eirst allstate, all-mode simulation of EET in the bacterial LH2 complex at room temperature. From this simulation, we discovered that the remarkable (~90%) efEiciency and (~1 ps) timescale of EET that have been observed experimentally are enabled by the two-ring arrangement of chromophores and quantum effects associated with nuclear vibrations. I will brieEly browse through some other consequences of strong electron-vibration couplings – e.g., on the decay of vibronic coherences in cofacial porphyrin dimers[4], and on the scrambling of information [5] by chemical reactions. Section 2 will focus on the chemical dynamics of battery systems [6-8]. Aside from the complexities of nuclear motion in the electrolyte solution and at the electrode-electrolyte interface, the dynamics of such systems are riddled by the external electric Eield and potentially non-adiabatic electron transfer at the electrodes. I will Eirst describe our recent theory [6] of reaction rates for electric Eield catalysis in polar solvents. I will then go on to discuss a new approximate method[7] to compute charge-transfer excitations for molecular and metal-molecule interfacial systems using ground state force Eields that allow dynamic Eluctuations of atomic charges. Finally, and if time permits, I might share an overview of ongoing work on the need to account for explicit dynamics and solvation when studying the formation of the solid-electrolyte interphase (SEI) in lithium-based batteries. Using ab initio molecular dynamics calculations, we have developed neural network potentials that are allowing us to compute free energies and exact (classical) rates for electrolyte decomposition on the lithium surface [8] at Eirst-principles accuracy.
References:
 
[1] S. Kundu and N. Makri, “Modular Path Integral for Finite-Temperature Dynamics of Extended Systems with Intramolecular Vibrations”, Journal of Chemical Physics 153, 044124 (2020)
  1. [2] S. Kundu and N. Makri, “Small Matrix Quantum Classical Path Integral”, Journal of Physical Chemistry Letters, 13, 3492-3498 (2022)
  2. [3] S. Kundu, R. Dani and N. Makri, “Tight Inner Ring Architecture and Quantum Motion of Nuclei Enable Ef[icient Energy Transfer in Bacterial Light Harvesting”, Science Advances, 8, eadd0023 (2022) [4] P. P. Roy, S. Kundu, et al, “Synthetic Control of Exciton Dynamics in Bioinspired Cofacial Porphyrin Dimers, Journal of the American Chemical Society, 144, 14, 6298–6310 (2022)
  3. [5] C. Zhang#, S. Kundu#, N. Makri, M. Gruebele, and P. Wolynes, “Quantum Information Scrambling and Chemical Reactions”, Proceedings of the National Academy of Sciences, 121 (15) e2321668121 (2024). # denotes authors contributed equally.
  4. [6] S. Kundu and T.C. Berkelbach, “Reaction Rate Theory for Electric Field Catalysis in Solution”, Journal of the American Chemical Society 146, 38, 26041–26047 (2024)
  5. [7] S. Kundu*, H.-Z. Ye, and T.C. Berkelbach*, “Charge-Transfer Excitations from Fluctuating Charge Force Fields”, (2024) (in preparation). * denotes corresponding author.
  6. [8]. S. Kundu*, D. Chamaki, H.-Z. Ye, and T.C. Berkelbach*, “Reaction Dynamics and Rates for the Decomposition of Ethylene Carbonate on Lithium using Fine-Tuned Machine Learning Potentials”, (2024) (in preparation).