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KinBot: An automated journey from a single structure to rate coefficients. – (Clément Soulié / LCPQ/ Séminaire). – 19/12/2024, 14H00

19 December; 14h00 - 16h00

Séminaire LCPQ

Clément Soulié, Sandia National Laboratories, CA

Summary:
With the advances in computer power and improved robustness of quantum chemistry calculations, it is now possible to automate those in order to provide the necessary data for rate coefficients calculations using master equation solvers. This is the objective of KinBot [1], an open-source software developed by the group of Judit Zádor at Sandia National Laboratories.

KinBot explores the potential energy surface (PES) of a system starting from an initial structure by proposing transition state (TS) geometries based on reaction templates, selected depending on the patterns present in the initial well. If the connectivity between the new product and the initial well is established (IRC), a new search starts from the product. Each structure also undergoes a conformational minimization, as well as hindered rotors scans and the results can be wrapped in an input for subsequent Master Equation calculation of the temperature and pressure dependent rate coefficients of each reaction.

However, calculating the rate coefficients for barrierless bimolecular capture reactions remain a challenge as the rigid-rotor/harmonic approximation is a bad approximation to describe the normal modes along the reaction coordinate. Hence, among the recent advances, KinBot has been extended to prepare variable reaction coordinate transition state theory (VRC-TST) calculations with a new interface to rotdPy[2], a PES sampler for VRC-TST.

[1] Judit Zádor, Carles Martí, Ruben Van de Vijver, Sommer L. Johansen, Yoona Yang, Hope A. Michelsen, Habib N. Najm: Automated reaction kinetics of gas-phase organic species over multiwell potential energy surfaces, J. Phys. Chem. A, 2023, 127, 565–588. https://pubs.acs.org/doi/10.1021/acs.jpca.2c06558

[2] Xi Chen and C. Franklin Goldsmith: Accelerating Variational Transition State Theory via Artificial Neural Networks, J. Phys. Chem. A, 2020, 124 (5), 1038-1046. https://pubs.acs.org/doi/10.1021/acs.jpca.9b11507

https://github.com/zadorlab/KinBot 

https://www.jzador.xyz/


Details

Date:
19 December
Time:
14h00 - 16h00
Event Categories:
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Venue

Salle de conférence, Bâtiment 3R4

Organiser

LCPQ
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