CHAMP-EU

The Cornell-Holland Ab-initio Materials Package (CHAMP) is a quantum Monte Carlo suite of programs for electronic structure calculations. The code is developed by Claudia Filippi and Saverio Moroni, with significant contributions by Ravindra Shinde, N. Renaud, V. Azizi, E. Landinez, and S. Shepard.

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What CHAMP-EU can do for you

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CHAMP release build with Intel CHAMP debug build with Intel and GNU CHAMP with QMCkl and TREXIO build with Intel Build Docker Image Publish Docker image TREXIO python interface

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The Cornell-Holland Ab-initio Materials Package (CHAMP) is a quantum Monte Carlo suite of programs for electronic structure calculations of atomic and molecular systems. The code is a sister code of the homonymous program originally developed by Cyrus Umrigar and Claudia Filippi of which it retains the accelerated Metropolis method and the efficient diffusion Monte Carlo algorithms.

The European branch of the code is currently developed by Claudia Filippi and Saverio Moroni, with significant contributions by Ravindra Shinde, Nicolas Renaud, Victor Azizi, Edgar Landinez, and Stuart Shepard.

CHAMP has three basic capabilities:

  • Metropolis or variational Monte Carlo (VMC)
  • Diffusion Monte Carlo (DMC)
  • Optimization of many-body wave functions by energy minimization (VMC) for ground and excited states

Noteworthy features of CHAMP are:

  • Efficient wave function optimization also in a state-average and a state-specific fashion for multiple states of the same symmetry (VMC)
  • Efficient computation of analytical interatomic forces (VMC)
  • Compact formulation for a fast evaluation of multi-determinant expansions and their derivatives (VMC and DMC)
  • Multiscale VMC and DMC calculations in classical point charges (MM), polarizable continuum model (PCM), and polarizable force fields (MMpol)

Note

The code is available for free under the GPL-3.0 license. Developers and contributors are welcome to use and contribute back to the code. If you have used the code for your publications, please cite this source.

Usual disclaimer

The authors make no claims about the correctness of the program suite and people who use it do so at their own risk.


CHAMP utilizes various other program packages:

  1. Parser: An easy-to-use and easy-to-extend keyword-value pair-based input file parser written in Fortran 2008. This parser uses a heavily modified libFDF library and is written by Ravindra Shinde. It can parse keyword-value pairs, blocks of data, and general variables with different physical units in an order-independent manner. Our implementation can handle multiple data types and file formats. The parser is kept as a library in the code, however, it can be easily adapted by any other Fortran-based code.

  2. TREXIO: TREXIO is an open-source file format and library developed for the storage and manipulation of data produced by quantum chemistry calculations. CHAMP can read the starting wavefunction from a trexio file. The library has interfaces to a lot of quantum chemical programs. CHAMP can directly read the contents of this file with a single load statement in the input file. This library is currently optional.

  1. TREXIO Tools: We provide a Python package inside the CHAMP's tool directory to extract all the necessary information from a TREXIO file in the hdf5 file format to a human-readable text format. This allows one to bypass the TREXIO library within CHAMP and input the necessary data via the Parser (see Option 2 in Section "Preparing the Input File" below).

  2. QMCKL: This library provides a high-performance implementation of the main kernels of Quantum Monte Carlo methods. This library is currently optional.


CHAMP in a container

CHAMP is available as a container image from Dockerhub. Here are the instructions to obtain the images:

  1. CHAMP built with Intel oneAPI compilers:
    • docker pull neelravi/champ:latest
    • docker pull neelravi/champ:intel
    • docker pull neelravi/champ:intel-trexio
  2. CHAMP built with GNU compilers
    • docker pull neelravi/champ:2.3.0
    • docker pull neelravi/champ:gnu
    • docker pull neelravi/champ:gnu-trexio

Compiling CHAMP for the source

Requirements

  1. cmake >= 3.17
  2. gfortran/gcc >= 9.3.0 or Intel Fortran 2020 onwards
  3. BLAS/LAPACK or Intel MKL
  4. openMPI >= 3.0 or Intel MPI
  5. [Optional] TREXIO library >= 2.4.0
  6. [Optional] QMCkl library >= 1.0.0
  7. [Optional] doxygen (for documentation)

Installation Using CMake

To install Champ using cmake you need to run the following commands:

cmake -H. -Bbuild
cmake --build build -- -j4

The first command is only required to set up the build directory and needs to be executed only once. Compared to the previous Makefiles the dependencies for the include files (e.g include/vmc.h) are correctly setup and no --clean-first is required.

CMAKE Options

To select a given compiler, you can type:

cmake -H. -Bbuild -D CMAKE_Fortran_COMPILER=mpif90

To use LAPACK and BLAS installed locally, include the path to the libraries:

cmake -H. -Bbuild \
	-DCMAKE_Fortran_COMPILER=mpif90 \
	-DBLAS_blas_LIBRARY=/home/user/lib/BLAS/blas_LINUX.a \
	-DLAPACK_lapack_LIBRARY=/home/user/lib/LAPACK/liblapack.a

To enable/disable vectorization based on the architecture:

cmake -H. -Bbuild -DCMAKE_Fortran_COMPILER=mpif90 -DVECTORIZED=yes/no/auto

To compile only e.g. VMC serial:

cmake --build build --target vmc.mov1

Clean and build:

cmake --build build --clean-first

CMAKE Recipes

Here are a couple of recipes for commonly used computing facilities, which can be easily adapted.

  • Snellius (snellius.surfa.nl):
    • To compile the code, first load the required modules:

      module purge
      module load 2022
      module load intel/2022a
      module load HDF5/1.12.2-iimpi-2022a
      

      then set-up the build:

      cmake -H. -Bbuild -DCMAKE_Fortran_COMPILER=mpiifort
      

      Optionally, you may link the trexio library using the following command:

      cmake -S. -Bbuild  \
      	-DCMAKE_Fortran_COMPILER=mpiifort  \
      	-DENABLE_TREXIO=ON  \
      	-DTREXIO_LIBRARY=$HOME/lib/libtrexio.so  \
      	-DTREXIO_INCLUDE_DIR=$HOME/include/
      

      and finally build:

      cmake --build build -j8 --clean-first
      
    • To run the code, you need to submit a job to the queue system:

      sbatch job.cmd
      

      where job.cmd is a SLURM script for genoa partition that looks like this:

      #!/bin/bash
      #SBATCH -t 0-12:00:00            # time in (day-hours:min:sec)
      #SBATCH -N 1                     # number of nodes (change this number to use more nodes)
      #SBATCH --ntasks-per-node 192    # tasks per node (Use 192 for genoa and 128 for rome partition)
      #SBATCH -J vmc                   # name of the job
      #SBATCH -o vmc.%j.out            # std output file name for slurm
      #SBATCH -e vmc.%j.err            # std error file name for slurm
      #SBATCH --exclusive              # specific requirements about node
      #SBATCH --partition genoa        # partition (queue)
      #
      module purge
      module load 2022
      module load intel/2022a
      module load HDF5/1.12.2-iimpi-2022a
      #
      export I_MPI_PMI_LIBRARY=/usr/lib64/libpmi2.so
      cd $PWD
      srun champ/bin/vmc.mov1 -i input.inp -o output.out -e error
      
  • CCPhead:
    • To build with mpiifort, load the required modules of the Intel Compiler and MPI:

      module load cmake/latest
      module load compiler-rt/latest
      module load debugger/latest
      module load compiler/latest
      module load icc/latest
      module load mpi/latest
      module load hdf5/latest
      module load tbb/latest
      module load dpl/latest
      module load dev-utilities/latest
      module load mkl/latest
      module load trexio/latest
      

      Setup the build:

      cmake -H. -Bbuild -DCMAKE_Fortran_COMPILER=mpiifort
      
    • To enable TREXIO library:

      cmake -H. -Bbuild  \
      	-DCMAKE_Fortran_COMPILER=mpiifort -DENABLE_TREXIO=ON  \
      	-DTREXIO_LIBRARY=/software/libraries/trexio/latest/lib/libtrexio.so  \
      	-DTREXIO_INCLUDE_DIR=/software/libraries/trexio/latest/include/
      
    • To disable vectorization of the code:

      cmake -H. -Bbuild -DCMAKE_Fortran_COMPILER=mpiifort -DVECTORIZED=no
      
    • To run the code, you need to submit a job to the queue system:

      sbatch job.cmd
      

      where job.cmd is a SLURM script for genoa partition that looks like this:

      #!/bin/bash
      #SBATCH -t 2-0
      #SBATCH -p ccp22
      #SBATCH -N 2 --exclusive --ntasks-per-node 32
      #SBATCH -J champ
      #SBATCH --output=o%j
      #SBATCH --ntasks-per-core=1
      #SBATCH --error=e%j
      
      module load compiler-rt/latest
      module load debugger/latest
      module load compiler/latest
      module load icc/latest
      module load mpi/latest
      module load hdf5/latest
      module load tbb/latest
      module load dpl/latest
      module load dev-utilities/latest
      module load mkl/latest
      module load trexio/latest
      
      cd $PWD
      mpirun -np 64 champ/bin/vmc.mov1 -i input.inp -o output.out -e error
      
    • To build with gfortran:

      Setup the build:

      cmake -H. -Bbuild -DCMAKE_Fortran_COMPILER=/usr/bin/mpif90
      

      which will use LAPACK & BLAS from the Ubuntu repository. (Cmake should find them already if none of the Intel MKL variables are set.) Combining gfortran with the Intel MKL is possible but requires special care to work with the compiler flag -mcmodel=large.

    • To run the code:

      mpirun -s all -np "n process" -machinefile "machinefile"
      
  • Ubuntu desktop:
    • Ubuntu 20: Install the required packages:
      sudo apt install gfortran openmpi-bin libopenmpi-dev gawk libblacs-mpi-dev liblapack-dev
      
      Set-up the build:
      cmake -H. -Bbuild -DCMAKE_Fortran_COMPILER=mpifort
      
      Build:
      cmake --build build -- -j2
      
      To run in parallel:
      mpirun --stdin all -n 2 path_to_CHAMP/bin/vmc.mov1 -i vmc.inp -o vmc.out -e error
      
    • Ubuntu 18: Install the dependencies using conda instead of apt
    • WSL: The code also compiles on WSL.

User's manual and documentation

The user's manual and documentation is hosted at https://trex-coe.github.io/champ-user-manual/

Preparing the input files

CHAMP needs the following input files to describe a system

  1. Geometry
  2. ECP / Pseudopotentials
  3. Basis Set (Radial Grid files)
  4. Basis pointers
  5. MO coefficients
  6. Determinants and/or CSF files
  7. Molecular orbital symmetries (Optional)
  8. Molecular orbital eigenvalues (Optional)
  9. Jastrow parameters file
  10. Jastrow derivative parameters file (Optional)

CHAMP input file itself has a modular structure. For example,

1. general
2. electrons
3. blocking_vmc
4. blocking_dmc
5. optwf
6. ...

Option 1 (Using trexio file)

We can use trexio file (in hdf5 or text backend format) to specify all the inputs (except Jastrow and Jastrow derivatives)

A sample input file would look like:

%module general
    title           'VMC Calculation for a molecule'
    pool            './pool/'
    mode            'vmc_one_mpi'
    ipr 	    -1
%endmodule

load trexio          molecule.hdf5
load determinants    determinants.det
load jastrow         jastrow.jas

%module electrons
    nup           20
    nelec         40
%endmodule


%module blocking_vmc
    vmc_nstep     20
    vmc_nblk      100000
    vmc_nblkeq    1
    vmc_nconf_new 0
%endmodule

Obtaining a trexio file from GAMESS-US output

Make sure that the recent version of trexio_tools has been installed.

pip install trexio_tools

This will provide trexio executable in the path. Use the following command to generate a trexio file.

trexio convert-from --type gamess --input gamess_output.out --motype "RHF" victor.hdf5 --back_end=HDF5

Allowed values of MOtype are 'RHF', 'ROHF', 'MCSCF', 'NATURAL', 'GUGA' ...

NOTE : Use trexio --help for a verbose list of options.

Option 2 (Specification using individual text files)

The trexio file can be converted into several text files to be used with CHAMP. The Python converter is provided in the CHAMP's repository in the champ/tools/trex_tools folder.

A sample script is given below:

python3 /home/user/champ/tools/trex_tools/trex2champ.py \
	--trex 	"COH2_GS.trexio" \
	--backend	"HDF5" \
	--basis_prefix  "BFD-aug-cc-pVDZ" \
	--lcao \
	--ecp \
	--sym \
	--geom \
	--basis \
	--det

NOTE : Use python3 trex2champ.py --help for a verbose list of options.

Molecular coordinates

Molecular coordinates can be provided directly in the vmc or dmc input files using the %block structure of the parser.

The following are the valid examples

  1. Geometry in the (XYZ in Bohr units) format with automatic Zvalence

%block molecule
10
# molecular complex (Symbol, X,Y,Z in Bohr)
  Si  -0.59659972  0.06162019  0.21100680
  S   -2.60025162 -2.54807062 -2.52884266
  S    2.14594449  2.17606672 -2.44253887
  S    1.75703132 -2.78062975  2.53564756
  S   -1.40663455  3.06742023  3.14712509
  H   -3.50597461  0.49044059  0.39864337
  H    0.96753971  3.57914102  3.86259992
  H   -0.57825615 -3.70197321 -3.52433897
  H    0.37416575  3.66039924 -3.47898554
  H   -0.21164931 -3.70953211  3.82669513
%endblock
  1. Geometry in the (XYZ in Bohr units) format with explicit Zvalence. This also allows different labels for the same element.

%block molecule
10
# molecular complex (Symbol, X,Y,Z in Bohr, Zvalence)
  Si   -0.59659972  0.06162019  0.21100680    4.0
  S    -2.60025162 -2.54807062 -2.52884266    6.0
  S     2.14594449  2.17606672 -2.44253887    6.0
  S     1.75703132 -2.78062975  2.53564756    6.0
  S    -1.40663455  3.06742023  3.14712509    6.0
  H1   -3.50597461  0.49044059  0.39864337    1.0
  H2    0.96753971  3.57914102  3.86259992    1.0
  H2   -0.57825615 -3.70197321 -3.52433897    1.0
  H2    0.37416575  3.66039924 -3.47898554    1.0
  H2   -0.21164931 -3.70953211  3.82669513    1.0
%endblock
  1. Geometry in the (XYZ in Bohr units) format to be read from a separate .xyz file.

%block molecule < molecule.xyz

  1. Geometry in the (XYZ in Bohr units) format to be read from a separate .xyz file.

load molecule molecule.xyz

ECP / Pseudopotential files

ECP or pseudopotential files have a fixed format. Most of the BFD ECP files can be found in the champ/pool/BFD/ECP_champ folder. The files generated from the trexio file can also be used (except if it is coming from GAMESS. In this case, GAMESS truncates the digits of ECP information in its output, so the trexio file will not have all the digits stored.)

File format: BFD ECP for Silicon

BFD.gauss_ecp.dat.Si

BFD Si pseudo
3
3
4.00000000 1 1.80721061
7.22884246 3 9.99633089
-13.06725590 2 2.50043232
1
21.20531613 2 2.26686403
1
15.43693603 2 2.11659661

These files are generally kept in the pool directory of the calculation folder. You just need to specify the name BFD in the general module of the CHAMP input file under the keyword pseudopot. There should be a file for each type of an atom.

%module general
    title           'VMC Calculation for a molecule'
    pool            './pool/'
    mode            'vmc_one_mpi'
    pseudopot       BFD
    basis           ccpVTZ
    ipr             -1
%endmodule

Basis set (Basis on the radial grid) files

Basis files have a fixed format. The files generated from the trex2champ converter can also be used as they are. These files are generally kept in the pool directory of the calculation folder. You just need to specify the name of the basis file (say, ccpVTZ) in the general module of the CHAMP input file under the keyword basis. This will read the file ccpVTZ.basis.Si for the element Si.

The top few lines of BFD-T.basis.C look like

9 3 2000 1.003000 20.000000 0
 0.000000000000e+00  5.469976184517e-01  2.376319920758e+00  5.557936498748e-01  3.412818210005e+00  2.206803021951e-01  8.610719484857e-01  3.738901952004e-01  3.289926074834e+00  1.106692909826e+00
 1.508957441883e-04  5.469976454488e-01  2.376319870895e+00  5.557936481942e-01  3.412817957941e+00  2.206803015581e-01  8.610719410992e-01  3.738901923954e-01  3.289925989316e+00  1.106692890335e+00
 ...

This means there are 9 radial shells in the basis set of carbon put on a radial grid of 2000 points (up to 20 bohr).

Basis pointers (formerly bfinfo) files

The new format of the basis pointers file is given below. This file should be kept in the pool directory. This file is generated automatically by the trex2champ.py converter.

# Format of the new basis information file champ_v3
# num_ao_per_center, n(s), n(p), n(d), n(f), n(g)
# Index of Slm (Range 1 to 35)
# Index of the column from numerical basis file
qmc_bf_info 1
54 4 4 3 2 0
1 1 1 1 2 3 4 2 3 4 2 3 4 2 3 4 5 6 7 8 9 10 5 6 7 8 9 10 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 11 12 13 14 15 16 17 18 19 20
1 2 3 4 5 5 5 6 6 6 7 7 7 8 8 8 9 9 9 9 9 9 10 10 10 10 10 10 11 11 11 11 11 11 12 12 12 12 12 12 12 12 12 12 13 13 13 13 13 13 13 13 13 13
35 4 3 2 1 0
1 1 1 1 2 3 4 2 3 4 2 3 4 5 6 7 8 9 10 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
1 2 3 4 5 5 5 6 6 6 7 7 7 8 8 8 8 8 8 9 9 9 9 9 9 10 10 10 10 10 10 10 10 10 10
end

Each unique type of atom will have a pair of lines in the basis pointers file.

The first line after the comments qmc_bf_info 1 is a specification line to make sure that we are reading the basis function information file.

The second line is for the first unique atom in the system. It contains the number of atomic orbitals for that atom, the number of s-type functions, the number of p-type functions, the number of d-type functions, the number of f-type functions, and the number of g-type functions. num_ao_per_center, n(s), n(p), n(d), n(f), n(g)

The third line gives the index of Slm (or real Ylm). The numbers depend on how many radial shells are there in the basis set.

The fourth line tells which column of the radial grid file to be read for the construction of MO from the AOs.

Molecular Orbitals file

This file contains the molecular orbital coefficients. These are arranged as [num_ao, num_mo] array. This file is obtained automatically from the trex2champ.py converter. Please note that the AOs in this file follow the trexio convention of AO ordering.

For example, Four p-type shells of AOs will be arranged alphabetically as

X Y Z X Y Z X Y Z X Y Z

Two d-type shells of AOs will be arranged alphabetically as

XX XY XZ YY YZ ZZ XX XY XZ YY YZ ZZ

and so on.

The .lcao or .orb file has the following format.

lcao  226 200  1
...
...

end

The number 226 will be the number of AOs, 200 will be the number of orbitals, and 1 will be the number of types of orbitals.

Determinants and/or CSF file

The determinant file is automatically obtained from the trex2champ.py converter. Note that the trex2champ.py can also provide CSF and CSF map information if the corresponding GAMESS output file is provided with --gamess option.

The below is a typical file.

# Determinants, CSF, and CSF mapping from the GAMESS output / TREXIO file.
# Converted from the trexio file using trex2champ converter https://github.com/TREX-CoE/trexio_tools
determinants 36 1
-0.92276500 0.08745570 0.08745570 -0.03455773 -0.03455773 0.15892000 -0.00958342 -0.00958342 0.03141700 0.06827967 0.06827967 -0.02315988 -0.02315988 0.01639443 -0.00751472 0.00887972 0.00887972 -0.00751472 0.01639443 0.14336029 0.14336029 -0.06358518 -0.06358518 -0.00177625 -0.00177625 -0.01588657 -0.01588657 0.16425900 0.02504927 0.02504927 0.11380000 0.00560594 0.00560594 0.01069429 0.01069429 -0.04482000
   1    2    3    4    5    6    7    8    9   10   11      1    2    3    4    5    6    7    8    9   10   11
   1    2    3    4    5    6    7    8    9   10   11      1    2    3    4    5    6    7    8    9   10   13
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   1    2    3    4    5    6    7    8    9   11   12      1    2    3    4    5    6    7    8    9   12   13
   1    2    3    4    5    6    7    8    9   12   13      1    2    3    4    5    6    7    8    9   11   12
   1    2    3    4    5    6    7    8    9   12   13      1    2    3    4    5    6    7    8    9   12   13
end
csf 20 2
0.92276500 -0.12368100 0.04887200 -0.15892000 0.01355300 -0.03141700 -0.09656200 0.03275300 0.02839600 -0.20274200 -0.00136500 0.08992300 -0.00251200 -0.02246700 -0.16425900 -0.03542500 -0.11380000 0.00792800 0.01512400 0.04482000
0.13390600 -0.08999000 -0.04327600 0.07929200 0.06217900 -0.00658100 0.96025800 -0.00444100 0.01898800 0.15434900 -0.04594200 -0.01868700 0.00187600 0.04520300 -0.06578900 -0.04536600 0.04834200 -0.00269300 -0.04316900 -0.02239200
end
csfmap
20 36 40
1
  1  -1.000000
2
  2  -0.707107
  3  -0.707107
2
  4  -0.707107
  5  -0.707107
1
  6  -1.000000
2
  7  -0.707107
  8  -0.707107
1
  9  -1.000000
2
  10  -0.707107
  11  -0.707107
2
  12  -0.707107
  13  -0.707107
6
  14  0.577350
  15  -0.288675
  16  0.288675
  17  0.288675
  18  -0.288675
  19  0.577350
2
  20  -0.707107
  21  -0.707107
4
  15  -0.500000
  16  -0.500000
  17  -0.500000
  18  -0.500000
2
  22  -0.707107
  23  -0.707107
2
  24  0.707107
  25  0.707107
2
  26  0.707107
  27  0.707107
1
  28  -1.000000
2
  29  -0.707107
  30  -0.707107
1
  31  -1.000000
2
  32  0.707107
  33  0.707107
2
  34  0.707107
  35  0.707107
1
  36  -1.000000
end

Molecular orbital symmetries file [Optional; useful when doing orbital optimization]

This file is also generated using the trex2champ.py converter if the parent .hdf5 file contains the orbital symmetries.

A typical file looks like this:

sym_labels 4 226
 1 AG 2 AU 3 BG 4 BU
1 4 4 1 1 4 1 4 1 2 3 2 3 4 1 4 1 4 4 1 1 4 4 1 4 1 3 1 4 1 2 4 1 2 4 1 3 2 4 3 2 1 4 4 3 1 1 4 4 4 2 1 3 1 4 1 1 4 1 4 3 1 4 2 2 3 1 4 1 4 1 1 4 2 3 4 1 4 2 1 3 1 4 1 4 2 4 4 1 3 4 1 3 4 2 1 2 3 4 1 2 4 1 3 4 2 3 1 1 4 4 1 2 1 3 1 4 1 4 2 3 4 1 4 2 1 4 3 1 4 2 3 2 3 4 1 2 3 1 2 4 2 3 4 1 4 3 2 1 1 3 4 4 1 4 1 2 4 1 3 1 2 4 4 4 3 1 1 3 1 1 2 2 4 4 2 1 4 3 1 1 4 3 4 2 1 1 2 4 3 4 3 2 1 3 4 1 3 1 4 4 2 1 4 1 4 1 1 4 4 4 1 1 1 4 1 4 4 1 4 1 4 1 4 1 4
end

The numbers in front of irreducible representations are used as correspondence to identify the symmetry type of each orbital. Here in this case there are 226 molecular orbitals with 4 irreps.

Molecular orbital eigenvalues file [Optional]

This file is also generated using the trex2champ.py converter if the parent .hdf5 file contains the orbital eigenvalues.

A typical file looks like this:

# File created using the trex2champ converter https://github.com/TREX-CoE/trexio_tools
# Eigenvalues correspond to the RHF orbitals
eigenvalues 64
-1.3659 -0.7150 -0.5814 -0.5081 0.1201 0.1798 0.4846 0.5148 0.5767 0.6085 0.7153 0.7820 0.8691 0.8699 0.9642 1.2029 1.4091 1.4388 1.6082 1.6342 2.0787 2.1179 2.1776 2.2739 2.4123 2.5591 2.8217 3.3480 3.3840 3.4544 3.4607 3.6199 3.6237 3.9628 3.9661 4.0439 4.0481 4.2212 4.3500 4.4225 4.4577 4.5747 4.7271 4.8382 5.0086 5.5800 5.8020 6.0317 6.3754 6.5827 6.6970 6.7474 6.9245 7.0790 7.1820 7.2121 7.3257 7.3865 7.8607 8.4146 8.4733 9.0201 16.4980 27.1462
end

The first line contains the keyword eigenvalues followed by the number of orbitals. The following line contains eigenvalues as they appear in GAMESS or similar output. The file ends with the keyword end.

Jastrow parameters file

The Jastrow parameters can be provided using this file. It has the following format [Example: water].

jastrow_parameter   1
  5  5  0           norda,nordb,nordc
   0.60000000         scalek
   0.00000000   0.00000000  -0.41907755  -0.22916790  -0.04194614   0.08371252 (a(iparmj),iparmj=1,nparma)
   0.00000000   0.00000000  -0.09956809  -0.00598089   0.00503028   0.00600649 (a(iparmj),iparmj=1,nparma)
   0.50000000   0.36987319   0.06971895   0.00745636  -0.00306208  -0.00246314 (b(iparmj),iparmj=1,nparmb)
 (c(iparmj),iparmj=1,nparmc)
 (c(iparmj),iparmj=1,nparmc)
end

The set a should appear for each unique atom type (in the same order as in the .xyz file).

The set b should appear once.

Three-body Jastrow terms c should appear for each unique atom type (in the same order as in the .xyz file)

Jastrow derivatives file

The Jastrow derivative parameters can be provided using this file. It has the following format [Example: water].

jasderiv
4 4 5 15 15 0 0 nparma,nparmb,nparmc,nparmf
  3 4 5 6 (iwjasa(iparm),iparm=1,nparma)
  3 4 5 6 (iwjasa(iparm),iparm=1,nparma)
2 3 4 5 6 (iwjasb(iparm),iparm=1,nparmb)
3 5 7 8 9         11 13 14 15 16     17 18 20 21 23 (c(iparmj),iparmj=1,nparmc)
3 5 7 8 9         11 13 14 15 16     17 18 20 21 23 (c(iparmj),iparmj=1,nparmc)
end
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Keywords
Programming languages
  • C 69%
  • Fortran 19%
  • Nemerle 10%
  • Perl 1%
  • Python 1%
License
</>Source code
Packages
github.com
hub.docker.com

Participating organisations

University of Twente
Netherlands eScience Center

Contributors

CF
Claudia Filippi
SM
Saverio Moroni
Scuola Internazionale Superiore di Studi Avanzati
SS
Stuart Shepard
ELB
Edgar Josue Landinez Borda
Victor Azizi
Victor Azizi