A quantum Monte Carlo study on electron correlation in all-metal aromatic clusters MAl4− (M = Li, Na, K, Rb, Cu, Ag and Au)
Literature Information
Bráulio Gabriel A. Brito, J. N. Teixeira Rabelo, Ladir Cândido
Using fixed-node diffusion quantum Monte Carlo (FN-DMC) simulation we investigate the electron correlation in all-metal aromatic clusters MAl4− (with M = Li, Na, K, Rb, Cu, Ag and Au). The electron detachment energies and electron affinities of the clusters are obtained. The vertical electron detachment energies obtained from the FN-DMC calculations are in very good agreement with the available experimental results. Calculations are also performed within the Hartree–Fock approximation, density-functional theory (DFT), and the couple-cluster (CCSD(T)) method. From the obtained results, we analyse the impact of the electron correlation effects in these bimetallic clusters and find that the correlation of the valence electrons contributes significantly to the detachment energies and electron affinities, varying between 20% and 50% of their total values. Furthermore, we discuss the electron correlation effects on the stability of the clusters as well as the accuracy of the DFT and CCSD(T) calculations in the present systems.
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Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.











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