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Developing and applying efficient DD-vMCG method for nonadiabatic simulations

Christopoulou, Georgia; (2021) Developing and applying efficient DD-vMCG method for nonadiabatic simulations. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Gaussian wavepacket methods have been widely employed for the investigation of nonadiabatic molecular dynamics. The Direct Dynamics variational Multi-Configurational Gaussian (DD-vMCG) method provides a fully quantum mechanical solution to the time-dependent Schrödinger equation for the time evolution of nuclei with potential surfaces calculated on-the-fly using a quantum chemistry program. The first strand of this research study was to develop new, more efficient algorithms and improve the existing code for DD-vMCG aiming to increase both the accuracy and efficiency of this method. Thus, a new, efficient parallel algorithm to control the DD-vMCG database of quantum chemistry points is presented along with improvements to the interpolation scheme. Benchmark calculations on butatriene, allene and formamide showed that the new scheme is a very accurate, efficient and general method to employ for full-dimensional dynamics calculations. The aforementioned algorithm was then used to describe the photodissociation dynamics of phenol including all degrees of freedom, as the second strand of this research work was to explore more complex chemical systems. Full-dimensional quantum dynamics calculations including for the first time six electronic states, along with a detailed comparison with existing 3-state and 4-state models are presented. Including the fifth singlet excited state has been shown to be vital in unravelling the photodissociation of phenol. State population and flux analysis provided new insights into the decay mechanism of phenol confirming the idea of rapid relaxation to the ground state through the ¹ππ/1¹πσ* conical intersection. Finally, an effort to further improve the accuracy of DD-vMCG was made by employing a state-of-the-art approach where a Gaussian process regression scheme is introduced and machined-learned potential energy surfaces are obtained. All the findings suggest that this method could be promising to calculate potential energy surface matrix elements. However, further development is essential to take advantage of its benefits and to deal with the computational cost.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Developing and applying efficient DD-vMCG method for nonadiabatic simulations
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2021. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Chemistry
URI: https://discovery.ucl.ac.uk/id/eprint/10131050
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