Features of QMCPACK¶
Note that besides direct use, most features are also available via Nexus, an advanced workflow tool to automate all aspects of QMC calculation from initial DFT calculations through to final analysis. Use of Nexus is highly recommended for research calculations due to the greater ease of use and increased reproducibility.
Real-space Monte Carlo¶
The following list contains the main production-level features of QMCPACK for real-space Monte Carlo. If you do not see a specific feature that you are interested in, check the remainder of this manual or ask if that specific feature can be made available.
Variational Monte Carlo (VMC).
Diffusion Monte Carlo (DMC).
Reptation Monte Carlo.
Single and multideterminant Slater Jastrow wavefunctions.
Wavefunction updates using optimized multideterminant algorithm of Clark et al.
One, two, and three-body Jastrow factors.
Excited state calculations via flexible occupancy assignment of Slater determinants.
All electron and nonlocal pseudopotential calculations.
Casula T-moves for variational evaluation of nonlocal pseudopotentials (non-size-consistent and size-consistent variants).
Spin-orbit coupling from relativistic pseudopotentials following the approach of Melton, Bennett, and Mitas.
Support for twist boundary conditions and calculations on metals.
Wavefunction optimization using the “linear method” of Umrigar and coworkers, with an arbitrary mix of variance and energy in the objective function.
Blocked, low memory adaptive shift optimizer of Zhao and Neuscamman.
Gaussian, Slater, plane-wave, and real-space spline basis sets for orbitals.
Interface and conversion utilities for plane-wave wavefunctions from Quantum ESPRESSO (Plane-Wave Self-Consistent Field package [PWSCF]).
Interface and conversion utilities for Gaussian-basis wavefunctions from GAMESS, PySCF, and QP2. Many more are supported via the molden format and molden2qmc.
Easy extension and interfacing to other electronic structure codes via standardized XML and HDF5 inputs.
MPI parallelism, with scaling to millions of cores.
Fully threaded using OpenMP.
Highly efficient vectorized CPU code tailored for modern architectures. [MLC+17]
OpenMP-offload-based performance portable GPU implementation, see Supported GPU features for real space QMC.
Analysis tools for minimal environments (Perl only) through to Python-based environments with graphs produced via matplotlib (included with Nexus).
Auxiliary-Field Quantum Monte Carlo¶
The orbital-space Auxiliary-Field Quantum Monte Carlo (AFQMC) method is now also available in QMCPACK. The main input data are the matrix elements of the Hamiltonian in a given single particle basis set, which must be produced from mean-field calculations such as Hartree-Fock or density functional theory. A partial list of the current capabilities of the code follows. For a detailed description of the available features, see Auxiliary-Field Quantum Monte Carlo.
Phaseless AFQMC algorithm of Zhang et al. [ZK03].
Very efficient GPU implementation for most features.
“Hybrid” and “local energy” propagation schemes.
Hamiltonian matrix elements from (1) Molpro’s FCIDUMP format (which can be produced by Molpro, PySCF, and VASP) and (2) internal HDF5 format produced by PySCF (see AFQMC section below).
AFQMC calculations with RHF (closed-shell doubly occupied), ROHF (open-shell doubly occupied), and UHF (spin polarized broken symmetry) symmetry.
Single and multideterminant trial wavefunctions. Multideterminant expansions with either orthogonal or nonorthogonal determinants.
Fast update scheme for orthogonal multideterminant expansions.
Distributed propagation algorithms for large systems. Enables calculations where data structures do not fit on a single node.
Complex implementation for PBC calculations with complex integrals.
Sparse representation of large matrices for reduced memory usage.
Mixed and back-propagated estimators.
Specialized implementation for solids with k-point symmetry (e.g. primitive unit cells with k-points).
Supported GPU features for real space QMC¶
The Performance portable implementation implements real space QMC methods using OpenMP offload programming model and accelerated linear algebra libraries. Runs with good performance on NVIDIA and AMD GPUs, and the Intel GPU support is under development. Unlike the “legacy” implementation, it is feature complete and users may mix and match CPU-only and GPU-accelerated features. Using batched QMC drivers is required.
The Legacy implementation fully based on NVIDIA CUDA has been removed.
QMCPACK supports running on multi-GPU node architectures via MPI.
Supported GPU features:
VMC, WFOpt, DMC
periodic, mixed, open
on host now, being ported
3D B-spline orbitals
on host now, being ported
One-body Jastrow factors
Two-body Jastrow factors
Other Jastrow factors
Coulomb interaction PBC e-i
Coulomb interaction PBC e-e
Coulomb interaction OpenBC
Model periodic Coulomb (MPC)
Amrita Mathuriya, Ye Luo, Raymond C. Clay, III, Anouar Benali, Luke Shulenburger, and Jeongnim Kim. Embracing a new era of highly efficient and productive quantum monte carlo simulations. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '17, 38:1–38:12. New York, NY, USA, 2017. ACM. URL: http://doi.acm.org/10.1145/3126908.3126952, doi:10.1145/3126908.3126952.
Shiwei Zhang and Henry Krakauer. Quantum Monte Carlo Method using Phase-Free Random Walks with Slater Determinants. Physical Review Letters, 90(13):136401, April 2003. doi:10.1103/PhysRevLett.90.136401.