Abstract

This study reports on the prospect for the routine use of Quantum Monte Carlo (QMC) for the electronic structure problem, applying fixed-node Diffusion Monte Carlo (DMC) to generate highly accurate Born-Oppenheimer potential energy curves (PECs) for small molecular systems. The singlet ground electronic states of CO and N2 were used as test cases. The PECs obtained by DMC employing multiconfigurational trial wavefunctions were compared with those obtained by conventional high-accuracy electronic structure methods such as multireference configuration interaction and/or the best available empirical spectroscopic curves. The goal was to test whether a straightforward procedure using available QMC codes could be applied robustly and reliably. Results obtained with DMC codes were found to be in close agreement with the benchmark PECs, and the n3 scaling with the number of electrons (compared with n7 or worse for conventional high-accuracy quantum chemistry) could be advantageous depending on the system size. Due to a large pre-factor in the scaling, for the small systems tested here, it is currently still much more computationally intensive to compute PECs with QMC. Nevertheless, QMC algorithms are particularly well-suited to large-scale parallelization and are therefore likely to become more relevant for future massively parallel hardware architectures.

Department(s)

Chemistry

Research Center/Lab(s)

Center for High Performance Computing Research

Keywords and Phrases

Electronic States; Electronic Structure; Molecular Physics; Monte Carlo Methods; Quantum Chemistry; Born-Oppenheimer Potentials; Fixed Node Diffusion Monte Carlo; Ground Electronic State; Massively Parallels; Multi Reference Configuration Interactions; Number of Electrons; Potential Energy Curves; Quantum Monte Carlo; Potential Energy

International Standard Serial Number (ISSN)

0021-9606

Document Type

Article - Journal

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 2016 Andrew D. Powell and Richard Dawes, All rights reserved.

Publication Date

01 Jan 2016

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