aiida-pyscf 0.4.2

AiiDA plugin for the Python-based Simulations of Chemistry Framework (PySCF).

aiida-pyscf

An AiiDA plugin for the Python-based Simulations of Chemistry Framework (PySCF).

1. Installation
2. Requirements
3. Setup
4. Examples
   • Mean-field calculation
   • Customizing the structure
   • Optimizing geometry
   • Writing Hamiltonian to FCIDUMP files
   • Writing orbitals to CUBE files
   • Restarting unconverged calculations
   • Automatic error recovery
   • Pickled model

Installation

The recommended method of installation is through pip:

pip install aiida-pyscf

Requirements

To use aiida-pyscf a configured AiiDA profile is required. Please refer to the documentation of aiida-core for detailed instructions.

Setup

To run a PySCF calculation through AiiDA using the aiida-pyscf plugin, the computer needs to be configured where PySCF should be run. Please refer to the documentation of aiida-core for detailed instructions.

Then the PySCF code needs to be configured. The following YAML configuration file can be taken as a starting point:

label: pyscf
description: PySCF
computer: localhost
filepath_executable: python
default_calc_job_plugin: pyscf.base
use_double_quotes: false
with_mpi: false
prepend_text: ''
append_text: ''

Write the contents to a file named pyscf.yml, making sure to update the value of computer to the label of the computer configured in the previous step. To configure the code, execute:

verdi code create core.code.installed --config pyscf.yml -n

This should now have created the code with the label pyscf that will be used in the following examples.

Examples

Mean-field calculation

The default calculation is to perform a mean-field calculation. At a very minimum, the structure and the mean-field method should be defined:

from ase.build import molecule
from aiida.engine import run
from aiida.orm import Dict, StructureData, load_code

builder = load_code('pyscf').get_builder()
builder.structure = StructureData(ase=molecule('H2O'))
builder.parameters = Dict({'mean_field': {'method': 'RHF'}})
results, node = run.get_node(builder)

This runs a Hartree-Fock calculation on the geometry of a water molecule.

The main results are stored in the parameters output, which by default contain the computed total_energy and forces, details on the molecular orbitals, as well as some timing information:

print(results['parameters'].get_dict())
{
    'mean_field': {
        'forces': [
            [-6.4898366104394e-16, 3.0329042995656e-15, 2.2269765466236],
            [1.122487932593e-14, 0.64803103141326, -1.1134882733107],
            [-1.0575895664886e-14, -0.64803103141331, -1.1134882733108]
        ],
        'forces_units': 'eV/Å',
        'molecular_orbitals': {
            'labels': [
                '0 O 1s',
                '0 O 2s',
                '0 O 2px',
                '0 O 2py',
                '0 O 2pz',
                '1 H 1s',
                '2 H 1s'
            ],
            'energies': [
                -550.86280025028,
                -34.375426862456,
                -16.629598134599,
                -12.323304634736,
                -10.637428057751,
                16.200273277782,
                19.796075801491
            ],
            'occupations': [2.0, 2.0, 2.0, 2.0, 2.0, 0.0, 0.0]
        },
        'total_energy': -2039.8853743664,
        'total_energy_units': 'eV',
    },
    'timings': {
        'total': 1.3238215579768, 'mean_field': 0.47364449803717
    },
}

Customizing the structure

The geometry of the structure is fully defined through the structure input, which is provided by a StructureData node. Any other properties, e.g., the charge and what basis set to use, can be specified through the structure dictionary in the parameters input:

from ase.build import molecule
from aiida.engine import run
from aiida.orm import Dict, StructureData, load_code

builder = load_code('pyscf').get_builder()
builder.structure = StructureData(ase=molecule('H2O'))
builder.parameters = Dict({
    'mean_field': {'method': 'RHF'},
    'structure': {
        'basis ': 'sto-3g',
        'charge': 0,
    }
})
results, node = run.get_node(builder)

Any attribute of the pyscf.gto.Mole class which is used to define the structure can be set through the structure dictionary, with the exception of the atom and unit attributes, which are set automatically by the plugin based on the StructureData input.

Optimizing geometry

The geometry can be optimized by specifying the optimizer dictionary in the input parameters. The solver has to be specified, and currently the solvers geometric and berny are supported. The convergence_parameters accepts the parameters for the selected solver (see PySCF documentation for details):

from ase.build import molecule
from aiida.engine import run
from aiida.orm import Dict, StructureData, load_code

builder = load_code('pyscf').get_builder()
builder.structure = StructureData(ase=molecule('H2O'))
builder.parameters = Dict({
    'mean_field': {'method': 'RHF'},
    'optimizer': {
        'solver': 'geometric',
        'convergence_parameters': {
            'convergence_energy': 1e-6,  # Eh
            'convergence_grms': 3e-4,    # Eh/Bohr
            'convergence_gmax': 4.5e-4,  # Eh/Bohr
            'convergence_drms': 1.2e-3,  # Angstrom
            'convergence_dmax': 1.8e-3,  # Angstrom
        }
    }
})
results, node = run.get_node(builder)

The optimized structure is returned in the form of a StructureData under the structure output label. The structure and energy of each frame in the geometry optimization trajectory, are stored in the form of a TrajectoryData under the trajectory output label. The total energies can be retrieved as follows:

results['trajectory'].get_array('energies')

Localizing orbitals

To compute localized orbitals, specify the desired method in the parameters.localize_orbitals.method input:

from ase.build import molecule
from aiida.engine import run
from aiida.orm import Dict, StructureData, load_code

builder = load_code('pyscf').get_builder()
builder.structure = StructureData(ase=molecule('H2O'))
builder.parameters = Dict({
    'mean_field': {'method': 'RHF'},
    'localize_orbitals': {'method': 'ibo'}
})
results, node = run.get_node(builder)

The following methods are supported: boys, cholesky, edmiston, iao, ibo, lowdin, nao, orth, pipek, vvo. For more information, please refer to the PySCF documentation.

Computing the Hessian

In order to compute the Hessian, specify an empty dictionary for the hessian key in the parameters input:

from ase.build import molecule
from aiida.engine import run
from aiida.orm import Dict, StructureData, load_code

builder = load_code('pyscf').get_builder()
builder.structure = StructureData(ase=molecule('H2O'))
builder.parameters = Dict({
    'mean_field': {'method': 'RHF'},
    'hessian': {}
})
results, node = run.get_node(builder)

The computed Hessian will be attached as an ArrayData node with the link label hessian. Use node.outputs.hessian.get_array('hessian') to retrieve the computed Hessian as a numpy array for further processing.

Writing Hamiltonian to FCIDUMP files

To instruct the calculation to dump a representation of the Hamiltonian to FCIDUMP files, add the fcidump dictionary to the parameters input:

from ase.build import molecule
from aiida.engine import run
from aiida.orm import Dict, StructureData, load_code

builder = load_code('pyscf').get_builder()
builder.structure = StructureData(ase=molecule('N2'))
builder.parameters = Dict({
    'mean_field': {'method': 'RHF'},
    'fcidump': {
        'active_spaces': [[5, 6, 8, 9]],
        'occupations': [[1, 1, 1, 1]]
    }
})
results, node = run.get_node(builder)

The active_spaces and occupations keys are requires and each take a list of list of integers. For each element in the list, a FCIDUMP file is generated for the corresponding active spaces and the occupations of the orbitals. The shape of the active_spaces and occupations array has to be identical.

The generated FCIDUMP files are attached as SinglefileData output nodes in the fcidump namespace, where the label is determined by the index of the corresponding active space in the list:

print(results['fcidump']['active_space_0'].get_content())
 &FCI NORB=   4,NELEC= 4,MS2=0,
  ORBSYM=1,1,1,1,
  ISYM=1,
 &END
  0.5832127121682998       1    1    1    1
  0.5359642500498074       1    1    2    2
 -2.942091015256668e-15    1    1    3    2
  0.5381290185905914       1    1    3    3
 -3.782672959584676e-15    1    1    4    1
  ...

Generating CUBE files

The pyscf.tools.cubegen module provides functions to compute various properties of the system and write them as CUBE files. The PyscfCalculation plugin currently supports computing the following:

• molecular orbitals
• charge density
• molecular electrostatic potential

To instruct the calculation to dump a representation of any of these quantities to CUBE files, add the cubegen dictionary to the parameters input:

from ase.build import molecule
from aiida.engine import run
from aiida.orm import Dict, StructureData, load_code

builder = load_code('pyscf').get_builder()
builder.structure = StructureData(ase=molecule('N2'))
builder.parameters = Dict({
    'mean_field': {'method': 'RHF'},
    'cubegen': {
        'orbitals: {
            'indices': [5, 6],
            'parameters': {
                'nx': 40,
                'ny': 40,
                'nz': 40,
            }
        },
        'density': {
            'parameters': {
                'resolution': 300,
            }
        },
        'mep': {
            'parameters': {
                'resolution': 300,
            }
        }
    }
})
results, node = run.get_node(builder)

The indices key has to be specified for the orbitals subdictionary and takes a list of integers, indicating the indices of the molecular orbitals that should be written to file. Additional parameters can be provided in the parameters subdictionary (see the PySCF documentation for details). The parameters subdictionaries for the density and mep dictionaries are optional. To compute the charge density and molecular electrostatic potential, the and empty dictionary for the density and mep keys, respectively, is sufficient.

The generated CUBE files are attached as SinglefileData output nodes in the cubegen namespace, with the orbitals, density and mep subnamespaces. For the orbitals subnamespace, the label is determined by the corresponding molecular orbital index:

print(results['cubegen']['orbitals']['mo_5'].get_content())
Orbital value in real space (1/Bohr^3)
PySCF Version: 2.1.1  Date: Sun Apr  2 15:59:19 2023
    2   -3.000000   -3.000000   -4.067676
   40    0.153846    0.000000    0.000000
   40    0.000000    0.153846    0.000000
   40    0.000000    0.000000    0.208599
    7    0.000000    0.000000    0.000000    1.067676
    7    0.000000    0.000000    0.000000   -1.067676
 -1.10860E-04 -1.56874E-04 -2.16660E-04 -2.92099E-04 -3.84499E-04 -4.94299E-04
 -6.20809E-04 -7.62048E-04 -9.14724E-04 -1.07439E-03 -1.23579E-03 -1.39331E-03
  ...

Warning PySCF is known to fail when computing the MEP with DHF, DKS, GHF and GKS references.

Restarting unconverged calculations

The plugin will automatically instruct PySCF to write a checkpoint file. If the calculation did not converge, it will finish with exit status 410 and the checkpoint file is attached as a SinglefileData as the checkpoint output node. This node can then be passed as input to a new calculation to restart from the checkpoint:

failed_calculation = load_node(IDENTIFIER)
builder = failed_calculation.get_builder_restart()
builder.checkpoint = failed_calculation.outputs.checkpoint
submit(builder)

The plugin will write the checkpoint file of the failed calculation to the working directory such that PySCF can start of from there.

Post-processing

The PyscfCalculation plugin does not support all PySCF functionality; it aims to support most functionality that is computationally intensive, as in this case it is important to be able to offload these calculations as a calcjob on a remote compute resource. Most post-processing utilities are computationally inexpensive, and since the API is in Python, they can be called directly in AiiDA workflows as calcfunctions. Many PySCF utilities require the model of the system as an argument, where model is the main object used in PySCF, i.e. the object assigned to the mean_field variable in the following:

from pyscf import scf
mean_field = scf.RHF(..)
mean_field.kernel()

The kernel method is often computationally expensive, but its results (stored on the model object) are lost when the PyscfCalculation finishes as the Python interpreter of the calcjob shuts down and so the mean_field object no longer exists. This would force post-processing code to reconstruct the model from scratch and rerun the expensive kernel. Therefore, the PyscfCalculation serializes the PySCF model that was computed and stores it as a PickledData output node with the link label model in the provenance graph. This allows recreating the model in another Python interpreter and have it ready to be used for post-processing:

from pyscf.hessian import thermo
node = load_node()  # Load the completed `PyscfCalculation`
mean_field = node.outputs.model.load()  # Reconstruct the model by calling the `load()` method
hessian = mean_field.Hessian().kernel()
freq_info = thermo.harmonic_analysis(mean_field.mol, hessian)

Automatic error recovery

There are a variety of reasons why a PySCF calculation may not finish with the intended result. Examples are the self-consistent field cycle not converging or the job getting killed by the scheduler because it ran out of the requested walltime. The PyscfBaseWorkChain is designed to try and automatically recover from these kinds of errors whenever it can potentially be handled. It is a simple wrapper around the PyscfCalculation plugin that automatically restarts a new PyscfCalculation if the previous iterations failed. Launching a PyscfBaseWorkChain is almost identical to launching a PyscfCalculation directly; the inputs just have to be "nested" inside the pyscf namespace:

from aiida.engine import run
from aiida.orm import Dict, StructureData, load_code, load_node
from aiida_pyscf.workflows.base import PyscfBaseWorkChain
from ase.build import molecule

builder = PyscfBaseWorkChain.get_builder()
builder.pyscf.code = load_code('pyscf')
builder.pyscf.structure = StructureData(ase=molecule('H2O'))
builder.pyscf.parameters = Dict({
    'mean_field': {
        'method': 'RHF',
        'max_cycle': 3,
    }
})
results, node = run.get_node(builder)

In this example, we purposefully set the maximum number of iterations in the self-consistent field cycle to 3 ('mean_field.max_cycle' = 3), which will cause the first iteration to fail to reach convergence. The PyscfBaseWorkChain detects the error, indicated by exit status 410 on the PyscfCalculation, and automatically restarts the calculation from the saved checkpoint. After three iterations, the calculation converges:

$ verdi process status IDENTIFIER
PyscfBaseWorkChain<30126> Finished [0] [2:results]
    ├── PyscfCalculation<30127> Finished [410]
    ├── PyscfCalculation<30132> Finished [410]
    └── PyscfCalculation<30137> Finished [0]

The following error modes are currently handled by the PyscfBaseWorkChain:

• 120: Out of walltime: The calculation will be restarted from the last checkpoint if available, otherwise the work chain is aborted
• 140: Node failure: The calculation will be restarted from the last checkpoint
• 410: Electronic convergence not achieved: The calculation will be restarted from the last checkpoint
• 500: Ionic convergence not achieved: The geometry optmizization did not converge, calculation will be restarted from the last checkpoint and structure

Pickled model

The main objective of a PyscfCalculation is to solve the mean-field problem for a given structure. The results of this, often computationally expensive, step are stored in the mean_field_run variable in the main script:

mean_field = scf.RHF(structure)
density_matrix = mean_field.from_chk('restart.chk')
mean_field_run = mean_field.run(density_matrix)

The mean_field_run object can be used for a number of further post-processing operations implemented in PySCF. To keep the PyscfCalculation interface simple, not all of this functionality is supported. However, as soon as the calculation job finishes, the mean_field_run variable is lost and can no longer be accessed to be used for further processing.

As a workaround, the PyscfCalculation will "pickle" the mean_field_run object and attach it as the model output to the calculation. The model output node can be "unpickled" to restore the original mean_field_run object such that it can be used for further processing:

from aiida.engine import run
inputs = {}
results, node = run.get_node(PyscfCalculation, **inputs)
mean_field = node.outputs.model.load()
print(mean_field.e_tot)

Warning For certain cases, the calculation may fail to pickle the model and will except. In this case, one can set the pickle_model input to the PyscfCalculation to False.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [e-mail] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.