SILK QMC, sign-learning simulations of molecular systems

ORAL

Abstract

The Sign Learning Kink (SILK) based Quantum Monte Carlo (QMC) is used to calculate the ground state energies for H$_{2}$O, N$_2$ and F$_2$ molecules. This method is based on Feynman's path integral formalism and has two stages. The first, learning stage, reduces the minus sign problem by optimizing the Slater states which are used in the second, QMC stage. We test our method using different vector spaces and compare our results with other Quantum Chemical methods. We also perform exact diagonalization in those vector spaces as a benchmark. In each vector space and for each molecule, we perform SILK QMC for different bond lengths demonstrating that the SILK method is accurate for equilibrium and non-equilibrium geometries.

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Authors

  • Xiaoyao Ma

    Department of Physics and Astronomy, Louisiana State University

  • Frank Loffler

    Center for Computation and Technology, Louisiana State University

  • Karol Kowalski

    Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory

  • Randall Hall

    Department of Natural Sciences and Mathematics, Dominican University of California

  • Juana Moreno

    Louisiana State University, Louisiana State Univ - Baton Rouge, Department of Physics and Astronomy, Louisiana State University, Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA 70803, USA

  • Mark Jarrell

    Louisiana State University, Louisiana State Univ - Baton Rouge, Department of Physics \& Astronomy and Center for Computation \& Technology, Louisiana State University, Baton Rouge, LA 70803, USA, Department of Physics and Astronomy, Louisiana State University, Department of Physics and Astronomy, Louisiana State University, Baton Rouge, LA 70803, USA