mirror of https://github.com/QMCPACK/qmcpack.git
112 lines
4.3 KiB
ReStructuredText
112 lines
4.3 KiB
ReStructuredText
.. _chap:features:
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Features of QMCPACK
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===================
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Note that besides direct use, most features are also available via Nexus, an advanced workflow tool to automate all aspects of QMC
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calculation from initial DFT calculations through to final analysis. Use of Nexus is highly recommended for research calculations
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due to the greater ease of use and increased reproducibility.
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Real-space Monte Carlo
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----------------------
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The following list contains the main production-level features of QMCPACK for real-space Monte Carlo. If you do not see a specific
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feature that you are interested in, check the remainder of this manual or ask if that specific feature can be made available.
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- Variational Monte Carlo (VMC).
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- Diffusion Monte Carlo (DMC).
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- Reptation Monte Carlo.
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- Single and multideterminant Slater Jastrow wavefunctions.
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- Wavefunction updates using optimized multideterminant algorithm of
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Clark et al.
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- Backflow wavefunctions.
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- One, two, and three-body Jastrow factors.
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- Excited state calculations via flexible occupancy assignment of
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Slater determinants.
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- All electron and nonlocal pseudopotential calculations.
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- Casula T-moves for variational evaluation of nonlocal
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pseudopotentials (non-size-consistent and size-consistent variants).
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- Spin-orbit coupling from relativistic pseudopotentials following the
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approach of Melton, Bennett, and Mitas.
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- Support for twist boundary conditions and calculations on metals.
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- Wavefunction optimization using the “linear method” of Umrigar and
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coworkers, with an arbitrary mix of variance and energy in the objective
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function.
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- Blocked, low memory adaptive shift optimizer of Zhao and Neuscamman.
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- Gaussian, Slater, plane-wave, and real-space spline basis sets for
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orbitals.
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- Interface and conversion utilities for plane-wave wavefunctions from
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Quantum ESPRESSO (Plane-Wave Self-Consistent Field package [PWSCF]).
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- Interface and conversion utilities for Gaussian-basis wavefunctions
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from GAMESS, PySCF, and QP2. Many more are supported via the molden format and molden2qmc.
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- Easy extension and interfacing to other electronic structure codes
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via standardized XML and HDF5 inputs.
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- MPI parallelism, with scaling to millions of cores.
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- Fully threaded on CPUs using OpenMP.
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- Highly efficient vectorized CPU code tailored for modern architectures. :cite:`IPCC_SC17`
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- OpenMP-offload-based performance portable GPU implementation. Fully supports NVIDIA, AMD, and Intel GPUs.
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GPU and CPU execution can be mixed and matched.
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- Analysis tools for minimal environments (Perl only) through to
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Python-based environments with graphs produced via matplotlib (included with Nexus).
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Auxiliary-Field Quantum Monte Carlo
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-----------------------------------
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The orbital-space Auxiliary-Field Quantum Monte Carlo (AFQMC) method is now also available in QMCPACK. The main input data are the
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matrix elements of the Hamiltonian in a given single particle basis set, which must be produced from mean-field calculations such
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as Hartree-Fock or density functional theory. A partial list of the current capabilities of the code follows. For a detailed
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description of the available features, see :ref:`afqmc`.
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- Phaseless AFQMC algorithm of Zhang et al. :cite:`PhysRevLett.90.136401`.
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- Very efficient GPU implementation for most features.
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- “Hybrid" and “local energy" propagation schemes.
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- Hamiltonian matrix elements from (1) Molpro’s FCIDUMP format (which
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can be produced by Molpro, PySCF, and VASP) and (2) internal HDF5
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format produced by PySCF (see AFQMC section below).
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- AFQMC calculations with RHF (closed-shell doubly occupied), ROHF
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(open-shell doubly occupied), and UHF (spin polarized broken
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symmetry) symmetry.
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- Single and multideterminant trial wavefunctions. Multideterminant
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expansions with either orthogonal or nonorthogonal determinants.
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- Fast update scheme for orthogonal multideterminant expansions.
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- Distributed propagation algorithms for large systems. Enables
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calculations where data structures do not fit on a single node.
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- Complex implementation for PBC calculations with complex integrals.
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- Sparse representation of large matrices for reduced memory usage.
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- Mixed and back-propagated estimators.
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- Specialized implementation for solids with k-point symmetry (e.g.
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primitive unit cells with k-points).
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