Worked Example¶

Create Example Jobs¶

To begin with, create a variety of AMS jobs with different settings, engines and calculation types.

from scm.plams import from_smiles, AMSJob, PlamsError, Settings, Molecule, Atom
from scm.libbase import UnifiedChemicalSystem as ChemicalSystem
from scm.input_classes.drivers import AMS
from scm.input_classes.engines import DFTB
from scm.utils.conversions import plams_molecule_to_chemsys


def example_job_dftb(smiles, task, use_chemsys=False):
    # Generate molecule from smiles
    mol = from_smiles(smiles)
    if use_chemsys:
        mol = plams_molecule_to_chemsys(mol)

    # Set up calculation settings using PISA
    sett = Settings()
    sett.runscript.nproc = 1
    driver = AMS()
    driver.Task = task
    driver.Engine = DFTB()
    sett.input = driver
    return AMSJob(molecule=mol, settings=sett, name="dftb")


def example_job_adf(smiles, task, basis, gga=None, use_chemsys=False):
    # Generate molecule from smiles
    mol = from_smiles(smiles)
    if use_chemsys:
        mol = plams_molecule_to_chemsys(mol)

    # Set up calculation settings using standard settings
    sett = Settings()
    sett.runscript.nproc = 1
    sett.input.AMS.Task = task
    sett.input.ADF.Basis.Type = basis
    if gga:
        sett.input.ADF.XC.GGA = gga
    return AMSJob(molecule=mol, settings=sett, name="adf")


def example_job_neb(iterations, use_chemsys=False):
    # Set up molecules
    main_molecule = Molecule()
    main_molecule.add_atom(Atom(symbol="C", coords=(0, 0, 0)))
    main_molecule.add_atom(Atom(symbol="N", coords=(1.18, 0, 0)))
    main_molecule.add_atom(Atom(symbol="H", coords=(2.196, 0, 0)))
    final_molecule = main_molecule.copy()
    final_molecule.atoms[1].x = 1.163
    final_molecule.atoms[2].x = -1.078

    mol = {"": main_molecule, "final": final_molecule}

    if use_chemsys:
        mol = {k: plams_molecule_to_chemsys(v) for k, v in mol.items()}

    # Set up calculation settings
    sett = Settings()
    sett.runscript.nproc = 1
    sett.input.ams.Task = "NEB"
    sett.input.ams.NEB.Images = 9
    sett.input.ams.NEB.Iterations = iterations
    sett.input.DFTB

    return AMSJob(molecule=mol, settings=sett, name="neb")

Now, run a selection of them.

from scm.plams import config, JobRunner

config.default_jobrunner = JobRunner(parallel=True, maxthreads=8)

smiles = ["CC", "C", "O", "CO"]
tasks = ["SinglePoint", "GeometryOptimization"]
engines = ["DFTB", "ADF"]
jobs = []
for i, s in enumerate(smiles):
    for j, t in enumerate(tasks):
        job_dftb = example_job_dftb(s, t, use_chemsys=i % 2)
        job_adf1 = example_job_adf(s, t, "DZ", use_chemsys=True)
        job_adf2 = example_job_adf(s, t, "TZP", "PBE")
        jobs += [job_dftb, job_adf1, job_adf2]

job_neb1 = example_job_neb(10)
job_neb2 = example_job_neb(100, use_chemsys=True)
jobs += [job_neb1, job_neb2]

for j in jobs:
    j.run()

[11.02|17:42:50] JOB dftb STARTED
[11.02|17:42:50] JOB adf STARTED
[11.02|17:42:50] JOB adf STARTED
[11.02|17:42:50] JOB dftb STARTED
[11.02|17:42:50] JOB adf STARTED
[11.02|17:42:50] Renaming job adf to adf.002
[11.02|17:42:50] JOB adf STARTED
[11.02|17:42:50] JOB dftb STARTED
[11.02|17:42:50] JOB adf STARTED
[11.02|17:42:50] Renaming job dftb to dftb.002
... (PLAMS log lines truncated) ...

Job Analysis¶

Adding and Loading Jobs¶

Jobs can be loaded by passing job objects directly, or loading from a path.

from scm.plams import JobAnalysis

ja = JobAnalysis(jobs=jobs[:10], paths=[j.path for j in jobs[10:-2]])

[11.02|17:43:08] Waiting for job adf.003 to finish
[11.02|17:43:10] JOB neb FINISHED
[11.02|17:43:10] Job neb reported errors. Please check the output
[11.02|17:43:10] JOB neb FAILED
[11.02|17:43:10] Job neb reported errors. Please check the output
[11.02|17:43:10] Error message for job neb was:
    NEB optimization did NOT converge
[11.02|17:43:10] Job neb reported errors. Please check the output
[11.02|17:43:10] Job neb reported errors. Please check the output
[11.02|17:43:11] JOB neb.002 FINISHED
[11.02|17:43:11] JOB neb.002 SUCCESSFUL
[11.02|17:43:13] JOB adf.014 FINISHED
[11.02|17:43:13] JOB adf.014 SUCCESSFUL
[11.02|17:43:15] JOB adf.008 FINISHED
[11.02|17:43:15] JOB adf.008 SUCCESSFUL
[11.02|17:43:20] JOB adf.015 FINISHED
[11.02|17:43:20] JOB adf.015 SUCCESSFUL
... (PLAMS log lines truncated) ...
[11.02|17:43:22] Waiting for job adf.004 to finish

Jobs can also be added or removed after initialization.

ja.add_job(jobs[-2]).load_job(jobs[-1].path).display_table()

Path	Name	OK	Check	ErrorMsg
/path/plams/examples/JobAnalysis/plams_workdir/dftb	dftb	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf	adf	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.002	adf.002	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/dftb.002	dftb.002	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.003	adf.003	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.004	adf.004	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/dftb.003	dftb.003	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.005	adf.005	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.006	adf.006	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/dftb.004	dftb.004	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.007	adf.007	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.008	adf.008	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/dftb.005	dftb.005	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.009	adf.009	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.010	adf.010	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/dftb.006	dftb.006	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.011	adf.011	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.012	adf.012	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/dftb.007	dftb.007	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.013	adf.013	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.014	adf.014	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/dftb.008	dftb.008	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.015	adf.015	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/adf.016	adf.016	True	True	None
/path/plams/examples/JobAnalysis/plams_workdir/neb	neb	False	False	NEB optimization did NOT converge
/path/plams/examples/JobAnalysis/plams_workdir/neb.002	neb.002	True	True	None

Adding and Removing Fields¶

A range of common standard fields can be added with the add_standard_field(s) methods. Custom fields can also be added with the add_field method, by defining a field key, value accessor and optional arguments like display name and value formatting. Fields can be removed by calling remove_field with the corresponding field key.

(
    ja.remove_field("Path")
    .add_standard_fields(["Formula", "Smiles", "CPUTime", "SysTime"])
    .add_settings_input_fields()
    .add_field("Energy", lambda j: j.results.get_energy(unit="kJ/mol"), display_name="Energy [kJ/mol]", fmt=".2f")
    .display_table(max_rows=5)
)

Name	OK	Check	ErrorMsg	Formula	Smiles	CPUTime	SysTime	InputAmsTask	InputAdfBasisType	InputAdfXcGga	InputAmsNebImages	InputAmsNebIterations	Energy [kJ/mol]
dftb	True	True	None	C2H6	CC	0.210759	0.022143	SinglePoint	None	None	None	None	-19594.01
adf	True	True	None	C2H6	CC	4.274061	0.271191	SinglePoint	DZ	None	None	None	-3973.29
…	…	…	…	…	…	…	…	…	…	…	…	…	…
adf.016	True	True	None	CH4O	CO	20.258706	1.389218	GeometryOptimization	TZP	PBE	None	None	-2900.38
neb	False	False	NEB optimization did NOT converge	: CHN, final: CHN	: C=N, final: C#N	0.523557	0.087520	NEB	None	None	9	10	None
neb.002	True	True	None	: CHN, final: CHN	: C=N, final: C#N	0.869299	0.166113	NEB	None	None	9	100	-14936.53

In addition to the fluent syntax, both dictionary and dot syntaxes are also supported for adding and removing fields.

import numpy as np

ja["AtomType"] = lambda j: [at.symbol for at in j.results.get_main_molecule()]
ja.Charge = lambda j: j.results.get_charges()
ja.AtomCoords = lambda j: [np.array(at.coords) for at in j.results.get_main_molecule()]

del ja["Check"]
del ja.SysTime

ja.display_table(max_rows=5, max_col_width=30)

Name	OK	ErrorMsg	Formula	Smiles	CPUTime	InputAmsTask	InputAdfBasisType	InputAdfXcGga	InputAmsNebImages	InputAmsNebIterations	Energy [kJ/mol]	AtomType	Charge	AtomCoords
dftb	True	None	C2H6	CC	0.210759	SinglePoint	None	None	None	None	-19594.01	[‘C’, ‘C’, ‘H’, ‘H’, ‘H’, ‘H’,…	[-0.07293185 -0.07372966 0.02…	[array([-0.74763668, 0.041837…
adf	True	None	C2H6	CC	4.274061	SinglePoint	DZ	None	None	None	-3973.29	[‘C’, ‘C’, ‘H’, ‘H’, ‘H’, ‘H’,…	[-0.83243445 -0.83187828 0.27…	[array([-0.74763668, 0.041837…
…	…	…	…	…	…	…	…	…	…	…	…	…	…	…
adf.016	True	None	CH4O	CO	20.258706	GeometryOptimization	TZP	PBE	None	None	-2900.38	[‘C’, ‘O’, ‘H’, ‘H’, ‘H’, ‘H’]	[ 0.58673094 -0.60299606 -0.10…	[array([-0.36298962, -0.021487…
neb	False	NEB optimization did NOT conve…	: CHN, final: CHN	: C=N, final: C#N	0.523557	NEB	None	None	9	10	None	[‘C’, ‘N’, ‘H’]	None	[array([0.46287785, 0.20144834…
neb.002	True	None	: CHN, final: CHN	: C=N, final: C#N	0.869299	NEB	None	None	9	100	-14936.53	[‘C’, ‘N’, ‘H’]	[-0.00728359 -0.21164112 0.21…	[array([0.56171873, 0.20547696…

Processing Data¶

Once an initial analysis has been created, the data can be further processed, depending on the use case. For example, to inspect the difference between failed and successful jobs, jobs can be filtered down and irrelevant fields removed.

ja_neb = (
    ja.copy()
    .filter_jobs(lambda data: data["InputAmsTask"] == "NEB")
    .remove_field("AtomCoords")
    .remove_uniform_fields(ignore_empty=True)
)

ja_neb.display_table()

Name	OK	CPUTime	InputAmsNebIterations
neb	False	0.523557	10
neb.002	True	0.869299	100

Another use case may be to analyze the results from one or more jobs. For this, it can be useful to utilize the expand functionality to convert job(s) to multiple rows. During this process, fields selected for expansion will have their values extracted into individual rows, whilst other fields have their values duplicated.

ja_adf = (
    ja.copy()
    .filter_jobs(
        lambda data: data["InputAmsTask"] == "GeometryOptimization"
        and data["InputAdfBasisType"] is not None
        and data["Smiles"] == "O"
    )
    .expand_field("AtomType")
    .expand_field("Charge")
    .expand_field("AtomCoords")
    .remove_uniform_fields()
)

ja_adf.display_table()

Name	CPUTime	InputAdfBasisType	InputAdfXcGga	Energy [kJ/mol]	AtomType	Charge	AtomCoords
adf.011	2.780308	DZ	None	-1316.30	O	-0.8416865250737331	[-2.17062120e-04 3.82347777e-01 0.00000000e+00]
adf.011	2.780308	DZ	None	-1316.30	H	0.42084716070260286	[-0.81250923 -0.19167629 0. ]
adf.011	2.780308	DZ	None	-1316.30	H	0.4208393643711281	[ 0.8127263 -0.19067148 0. ]
adf.012	4.371058	TZP	PBE	-1363.77	O	-0.6739805275850443	[-2.46726007e-04 4.01580956e-01 0.00000000e+00]
adf.012	4.371058	TZP	PBE	-1363.77	H	0.33698188085180536	[-0.76455997 -0.2012764 0. ]
adf.012	4.371058	TZP	PBE	-1363.77	H	0.33699864673323343	[ 0.76480669 -0.20030455 0. ]

For more nested values, the depth of expansion can also be selected to further flatten the data.

(
    ja_adf.add_field("Coord", lambda j: [("x", "y", "z") for _ in j.results.get_main_molecule()], expansion_depth=2)
    .expand_field("AtomCoords", depth=2)
    .display_table()
)

Name	CPUTime	InputAdfBasisType	InputAdfXcGga	Energy [kJ/mol]	AtomType	Charge	AtomCoords	Coord
adf.011	2.780308	DZ	None	-1316.30	O	-0.8416865250737331	-0.00021706211955194217	x
adf.011	2.780308	DZ	None	-1316.30	O	-0.8416865250737331	0.38234777653349844	y
adf.011	2.780308	DZ	None	-1316.30	O	-0.8416865250737331	0.0	z
adf.011	2.780308	DZ	None	-1316.30	H	0.42084716070260286	-0.8125092343354401	x
adf.011	2.780308	DZ	None	-1316.30	H	0.42084716070260286	-0.19167629390344054	y
adf.011	2.780308	DZ	None	-1316.30	H	0.42084716070260286	0.0	z
adf.011	2.780308	DZ	None	-1316.30	H	0.4208393643711281	0.8127262964549918	x
adf.011	2.780308	DZ	None	-1316.30	H	0.4208393643711281	-0.19067148263005784	y
adf.011	2.780308	DZ	None	-1316.30	H	0.4208393643711281	0.0	z
adf.012	4.371058	TZP	PBE	-1363.77	O	-0.6739805275850443	-0.00024672600727009935	x
adf.012	4.371058	TZP	PBE	-1363.77	O	-0.6739805275850443	0.40158095623473306	y
adf.012	4.371058	TZP	PBE	-1363.77	O	-0.6739805275850443	0.0	z
adf.012	4.371058	TZP	PBE	-1363.77	H	0.33698188085180536	-0.7645599672263915	x
adf.012	4.371058	TZP	PBE	-1363.77	H	0.33698188085180536	-0.2012764045590436	y
adf.012	4.371058	TZP	PBE	-1363.77	H	0.33698188085180536	0.0	z
adf.012	4.371058	TZP	PBE	-1363.77	H	0.33699864673323343	0.7648066932336616	x
adf.012	4.371058	TZP	PBE	-1363.77	H	0.33699864673323343	-0.20030455167568945	y
adf.012	4.371058	TZP	PBE	-1363.77	H	0.33699864673323343	0.0	z

Expansion can be undone with the corresponding collapse method.

Fields can be also further filtered, modified or reordered to customize the analysis.

(
    ja_adf.collapse_field("AtomCoords")
    .collapse_field("Coord")
    .filter_fields(lambda vals: all([not isinstance(v, list) for v in vals]))  # remove arrays
    .remove_field("Name")
    .format_field("CPUTime", ".2f")
    .format_field("Charge", ".4f")
    .rename_field("InputAdfBasisType", "Basis")
    .reorder_fields(["AtomType", "Charge", "Energy"])
    .display_table()
)

AtomType	Charge	Energy [kJ/mol]	CPUTime	Basis	InputAdfXcGga
O	-0.8417	-1316.30	2.78	DZ	None
H	0.4208	-1316.30	2.78	DZ	None
H	0.4208	-1316.30	2.78	DZ	None
O	-0.6740	-1363.77	4.37	TZP	PBE
H	0.3370	-1363.77	4.37	TZP	PBE
H	0.3370	-1363.77	4.37	TZP	PBE

Extracting Analysis Data¶

Analysis data can be extracted in a variety of ways.

As has been demonstrated, a visual representation of the table can be easily generated using the to_table method (or display_table in a notebook). The format can be selected as markdown, html or rst. This will return the data with the specified display names and formatting.

print(ja_adf.to_table(fmt="rst"))

+----------+---------+-----------------+---------+-------+---------------+
| AtomType | Charge  | Energy [kJ/mol] | CPUTime | Basis | InputAdfXcGga |
+==========+=========+=================+=========+=======+===============+
| O        | -0.8417 | -1316.30        | 2.78    | DZ    | None          |
+----------+---------+-----------------+---------+-------+---------------+
| H        | 0.4208  | -1316.30        | 2.78    | DZ    | None          |
+----------+---------+-----------------+---------+-------+---------------+
| H        | 0.4208  | -1316.30        | 2.78    | DZ    | None          |
+----------+---------+-----------------+---------+-------+---------------+
| O        | -0.6740 | -1363.77        | 4.37    | TZP   | PBE           |
+----------+---------+-----------------+---------+-------+---------------+
| H        | 0.3370  | -1363.77        | 4.37    | TZP   | PBE           |
+----------+---------+-----------------+---------+-------+---------------+
| H        | 0.3370  | -1363.77        | 4.37    | TZP   | PBE           |
+----------+---------+-----------------+---------+-------+---------------+

Alternatively, raw data can be retrieved via the get_analysis method, which returns a dictionary of analysis keys to values.

print(ja_adf.get_analysis())

{'AtomType': ['O', 'H', 'H', 'O', 'H', 'H'], 'Charge': [-0.8416865250737331, 0.42084716070260286, 0.4208393643711281, -0.6739805275850443, 0.33698188085180536, 0.33699864673323343], 'Energy': [-1316.2997406426532, -1316.2997406426532, -1316.2997406426532, -1363.766294275197, -1363.766294275197, -1363.766294275197], 'CPUTime': [2.780308, 2.780308, 2.780308, 4.371058, 4.371058, 4.371058], 'InputAdfBasisType': ['DZ', 'DZ', 'DZ', 'TZP', 'TZP', 'TZP'], 'InputAdfXcGga': [None, None, None, 'PBE', 'PBE', 'PBE']}

Data can also be easily written to a csv file using to_csv_file, to be exported to another program.

csv_name = "./tmp.csv"
ja_adf.to_csv_file(csv_name)

with open(csv_name) as csv:
    print(csv.read())

AtomType,Charge,Energy,CPUTime,InputAdfBasisType,InputAdfXcGga
O,-0.8416865250737331,-1316.2997406426532,2.780308,DZ,
H,0.42084716070260286,-1316.2997406426532,2.780308,DZ,
H,0.4208393643711281,-1316.2997406426532,2.780308,DZ,
O,-0.6739805275850443,-1363.766294275197,4.371058,TZP,PBE
H,0.33698188085180536,-1363.766294275197,4.371058,TZP,PBE
H,0.33699864673323343,-1363.766294275197,4.371058,TZP,PBE

Finally, for more complex data analysis, the results can be converted to a pandas dataframe. This is recommended for more involved data manipulations, and can be installed using amspackages i.e. using the command: "${AMSBIN}/amspackages" install pandas.

try:
    import pandas

    df = ja_adf.to_dataframe()
    print(df)

except ImportError:

    print(
        "Pandas not available. Please install with amspackages to run this example '${AMSBIN}/amspackages install pandas'"
    )

  AtomType    Charge       Energy   CPUTime InputAdfBasisType InputAdfXcGga
      O -0.841687 -1316.299741  2.780308                DZ          None
      H  0.420847 -1316.299741  2.780308                DZ          None
      H  0.420839 -1316.299741  2.780308                DZ          None
      O -0.673981 -1363.766294  4.371058               TZP           PBE
      H  0.336982 -1363.766294  4.371058               TZP           PBE
      H  0.336999 -1363.766294  4.371058               TZP           PBE

Additional Analysis Methods¶

The JobAnalysis class does have some additional built in methods to aid with job analysis.

For example, the get_timeline and display_timeline methods show pictorially when jobs started, how long they took to run and what their status is.

This can be useful for visualizing the dependencies of jobs. Here you can see that the first 8 jobs started running in parallel, due to the maxthreads constraint, and the remaining jobs waited before starting. Also that the penultimate job failed.

ja.display_timeline(fmt="rst")

+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| JobName  | ↓2025-02-11 17:42:49 | ↓2025-02-11 17:43:06 | ↓2025-02-11 17:43:22 | ↓2025-02-11 17:43:39 | ↓2025-02-11 17:43:55 | WaitDuration | RunDuration | TotalDuration |
+==========+======================+======================+======================+======================+======================+==============+=============+===============+
| dftb     | ==>                  |                      |                      |                      |                      | 0s           | 1s          | 2s            |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf      | ===========>         |                      |                      |                      |                      | 0s           | 8s          | 9s            |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.002  | ==================== | >                    |                      |                      |                      | 0s           | 16s         | 17s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| dftb.002 | =*>                  |                      |                      |                      |                      | 0s           | 1s          | 1s            |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.003  | ==================== | ==================== | >                    |                      |                      | 0s           | 32s         | 33s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.004  | ==================== | ==================== | ==================== | ===================> |                      | 0s           | 1m5s        | 1m6s          |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| dftb.003 | =*>                  |                      |                      |                      |                      | 0s           | 0s          | 1s            |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.005  | =======>             |                      |                      |                      |                      | 0s           | 5s          | 5s            |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.006  | --==========>        |                      |                      |                      |                      | 0s           | 9s          | 10s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| dftb.004 | ..=>                 |                      |                      |                      |                      | 1s           | 0s          | 2s            |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.007  | ..===============>   |                      |                      |                      |                      | 1s           | 12s         | 14s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.008  | ..-================= | ===========>         |                      |                      |                      | 2s           | 23s         | 25s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| dftb.005 | ...----=>            |                      |                      |                      |                      | 2s           | 3s          | 6s            |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.009  | .......-====>        |                      |                      |                      |                      | 5s           | 4s          | 9s            |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.010  | ........---====>     |                      |                      |                      |                      | 6s           | 5s          | 12s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| dftb.006 | ...........-=>       |                      |                      |                      |                      | 8s           | 1s          | 10s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.011  | ............======>  |                      |                      |                      |                      | 9s           | 5s          | 15s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.012  | ............-======= | ==>                  |                      |                      |                      | 10s          | 8s          | 18s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| dftb.007 | .............--=>    |                      |                      |                      |                      | 10s          | 2s          | 13s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.013  | ...............-==== | ===>                 |                      |                      |                      | 12s          | 6s          | 18s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.014  | ................-=== | =========>           |                      |                      |                      | 13s          | 10s         | 23s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| dftb.008 | .................-=> |                      |                      |                      |                      | 14s          | 1s          | 15s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.015  | ..................-= | =================>   |                      |                      |                      | 15s          | 15s         | 30s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| adf.016  | ...................- | ==================== | ===========>         |                      |                      | 15s          | 26s         | 42s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| neb      | .................... | --===X               |                      |                      |                      | 16s          | 3s          | 20s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+
| neb.002  | .................... | ..-===>              |                      |                      |                      | 18s          | 2s          | 21s           |
+----------+----------------------+----------------------+----------------------+----------------------+----------------------+--------------+-------------+---------------+

Electronic Structure

ADF

Periodic DFT

DFTB & MOPAC

Interatomic Potentials

ReaxFF

Machine Learning Potentials

Force Fields

kMC and Microkinetics

Bumblebee: OLED stacks

Fluid Thermodynamics

COSMO-RS

Workflows and Utilities

OLED workflows

ChemTraYzer2

Conformers

Reactions Discovery

AMS Driver

Properties

PES Exploration

Molecular Dynamics

Monte Carlo

Interfaces

ParAMS

PLAMS

GUI

VASP

Downloads

Windows

Mac

Linux

Documentation

Overview

Tutorials

Installation Manual

Brochures

Other Resources

Changelog

Webinars

Workshops

Knowledgebank

FAQ

Pricing and licensing