aiida-pyscf 0.5.1
AiiDA plugin for the Python-based Simulations of Chemistry Framework (PySCF).
Requires Python: >=3.9
Keywords: aiida,workflows,pyscf

# `aiida-pyscf`

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An [AiiDA](https://www.aiida.net) plugin for the
[Python-based Simulations of Chemistry Framework (PySCF)](https://pyscf.org/index.html).

1. [Installation](#installation)
1. [Requirements](#requirements)
1. [Setup](#setup)
1. [Examples](#examples)
   - [Mean-field calculation](#mean-field-calculation)
   - [Customizing the structure](#customizing-the-structure)
   - [Optimizing geometry](#optimizing-geometry)
   - [Writing Hamiltonian to FCIDUMP files](#writing-hamiltonian-to-fcidump-files)
   - [Writing orbitals to CUBE files](#writing-orbitals-to-cube-files)
   - [Restarting unconverged calculations](#restarting-unconverged-calculations)
   - [Automatic error recovery](#automatic-error-recovery)
   - [Pickled model](#pickled-model)

## Installation

The recommended method of installation is through [`pip`](https://pip.pypa.io/en/stable/):

```
pip install aiida-pyscf
```

## Requirements

To use `aiida-pyscf` a configured AiiDA profile is required. Please refer to the
[documentation of `aiida-core`](https://aiida.readthedocs.io/projects/aiida-core/en/latest/intro/get_started.html) 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`](https://aiida.readthedocs.io/projects/aiida-core/en/latest/howto/run_codes.html#computer-setup)
for detailed instructions.

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

```yaml
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:

```bash
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:

```python
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:

```python
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:

```python
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](https://pyscf.org/user/gto.html) 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](https://pyscf.org/user/geomopt.html?highlight=geometry+optimization) for details):

```python
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:

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

### Localizing orbitals

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

```python
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](https://pyscf.org/user/lo.html).

### Computing the Hessian

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

```python
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:

```python
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:

```python
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:

```python
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](https://pyscf.org/pyscf_api_docs/pyscf.tools.html?highlight=fcidump#module-pyscf.tools.cubegen)
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:

```python
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:

```python
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 `calcfunction`s. 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:

```python
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:

```python
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:

```python
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:

```console
$ 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:

```python
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"](https://docs.python.org/3/library/pickle.html) 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:

```python
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

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For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact
[opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

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