A decomposition of the exact exchange-correlation potential of time-dependent density functional theory into an interaction component and a kinetic component offers a new starting point for non-adiabatic approximations. The components are expressed in terms of the exchange-correlation hole and the difference between the one-body density matrix of the interacting and Kohn-Sham systems, which must be approximated in terms of quantities accessible from the Kohn-Sham evolution. We explore several preliminary approximations, evaluate their fulfillment of known exact conditions, and test their performance on simple model systems for which available exact solutions indicate the significance of going beyond the adiabatic approximation.
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Chem Sci
January 2025
Leiden Institute of Chemistry, Gorlaeus Laboratories P. O. Box 9502 2300 RA Leiden The Netherlands
The accurate modeling of dissociative chemisorption of molecules on metal surfaces presents an exciting scientific challenge to theorists, and is practically relevant to modeling heterogeneously catalyzed reactive processes in computational catalysis. The first important scientific challenge in the field is that accurate barriers for dissociative chemisorption are not yet available from first principles methods. For systems that are not prone to charge transfer (for which the difference between the work function of the surface and the electron affinity of the molecule is larger than 7 eV) this problem can be circumvented: chemically accurate barrier heights can be extracted with a semi-empirical version of density functional theory (DFT).
View Article and Find Full Text PDFNanomaterials (Basel)
November 2024
Research Center for Structural Materials, National Institute for Materials Science (NIMS), 1-2-1 Sengen, Tsukuba 305-0047, Ibaraki, Japan.
Methane photolysis is a very important initiation reaction from the perspective of hydrogen production for alternative energy applications. In our recent work, we demonstrated using our recently developed novel method, non-adiabatic excited-state time-dependent GW (TDGW) molecular dynamics (MD), how the decomposition reaction of methane into a methyl radical and a hydrogen atom was captured accurately via the time-tracing of all quasiparticle levels. However, this process requires a large amount of photoabsorption energy (PAE ∼10.
View Article and Find Full Text PDFJ Chem Theory Comput
December 2024
Condensed Matter and Statistical Physics, The Abdus Salam International Centre for Theoretical Physics, 34151 Trieste, Italy.
Nonadiabatic molecular dynamics (NAMD) has become an essential computational technique for studying the photophysical relaxation of molecular systems after light absorption. These phenomena require approximations that go beyond the Born-Oppenheimer approximation, and the accuracy of the results heavily depends on the electronic structure theory employed. Sophisticated electronic methods, however, make these techniques computationally expensive, even for medium size systems.
View Article and Find Full Text PDFPhys Chem Chem Phys
October 2024
CNRS, Université de Bordeaux, ISM, UMR 5255, F-33400 Talence, France.
Quantum benchmark calculations of the H + HOD abstraction reaction [B. Zhao , , 2018, , 17029-17037] provide an opportunity to test approximate methods, such as quasi-classical trajectories (QCTs) with Gaussian binning. However, the large mode-specific enhancements of this reaction lead to special challenges and unphysical QCT cross-section artifacts.
View Article and Find Full Text PDFJ Chem Phys
October 2024
Department of Physics, Rutgers University, Newark, New Jersey 07102, USA.
We extend the DeePMD neural network architecture to predict electronic structure properties necessary to perform non-adiabatic dynamics simulations. While learning the excited state energies and forces follows a straightforward extension of the DeePMD approach for ground-state energies and forces, how to learn the map between the non-adiabatic coupling vectors (NACV) and the local chemical environment descriptors of DeePMD is less trivial. Most implementations of machine-learning-based non-adiabatic dynamics inherently approximate the NACVs, with an underlying assumption that the energy-difference-scaled NACVs are conservative fields.
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