Abstract
We review recent advances in the capabilities of the open source ab initio Quantum Monte Carlo (QMC) package QMCPACK and the workflow tool Nexus used for greater efficiency and reproducibility. The auxiliary field QMC (AFQMC) implementation has been greatly expanded to include k-point symmetries, tensor-hypercontraction, and accelerated graphical processing unit (GPU) support. These scaling and memory reductions greatly increase the number of orbitals that can practically be included in AFQMC calculations, increasing the accuracy. Advances in real space methods include techniques for accurate computation of bandgaps and for systematically improving the nodal surface of ground state wavefunctions. Results of these calculations can be used to validate application of more approximate electronic structure methods, including GW and density functional based techniques. To provide an improved foundation for these calculations, we utilize a new set of correlation-consistent effective core potentials (pseudopotentials) that are more accurate than previous sets; these can also be applied in quantum-chemical and other many-body applications, not only QMC. These advances increase the efficiency, accuracy, and range of properties that can be studied in both molecules and materials with QMC and QMCPACK.
| Original language | English |
|---|---|
| Article number | 174105 |
| Journal | Journal of Chemical Physics |
| Volume | 152 |
| Issue number | 17 |
| DOIs | |
| State | Published - May 7 2020 |
Funding
This manuscript has been authored by UT–Battelle, LLC, under Contract No. DE-AC0500OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for the United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under contract Grant No. DE-AC02-06CH11357. This research also used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract Grant No. DE-AC05-00OR22725. Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International, Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under Contract No. DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government. Methodological development and scientific applications of QMCPACK are currently primarily supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Sciences and Engineering Division, as part of the Computational Materials Sciences Program and the Center for Predictive Simulation of Functional Materials. Software developments focused on future Exascale architectures are supported by the Exascale Computing Project (Grant No. 17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. S.U. and K.D.J. acknowledge the support of NSF (Grant No. CHE-1762337). B.R. acknowledges previous support for the work described from the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract Grant Nos. DEAC52-07NA27344 and 15-ERD-013 and NSF (Grant No. DMR-1726213). L.Z. acknowledges additional funding from the Dalton Fellowship at the University of Washington. An award of computer time was provided by the Innovative and Novel Computational Impact on Theory and Experiment (INCITE) program.