Replication Data for: Static polarizabilities at the basis set limit: A benchmark of 124 species (doi:10.18710/KLQVOK)

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Replication Data for: Static polarizabilities at the basis set limit: A benchmark of 124 species

doi:10.18710/KLQVOK

DataverseNO

2020-03-09

4

Brakestad, Anders; Jensen, Stig Rune; Wind, Peter; D'Alessandro, Marco; Genovese, Luigi; Hopmann, Kathrin Helen; Frediani, Luca, 2020, "Replication Data for: Static polarizabilities at the basis set limit: A benchmark of 124 species", https://doi.org/10.18710/KLQVOK, DataverseNO, V4, UNF:6:pBXeXp0l5KVVijVHeSqsdg== [fileUNF]

Replication Data for: Static polarizabilities at the basis set limit: A benchmark of 124 species

doi:10.18710/KLQVOK

Brakestad, Anders (UiT The Arctic University of Norway)

Jensen, Stig Rune (UiT The Arctic University of Norway)

Wind, Peter (UiT The Arctic University of Norway)

D'Alessandro, Marco (Institute of Structure of Matter)

Genovese, Luigi (Laboratoire de Simulation Atomistique)

Hopmann, Kathrin Helen (UiT The Arctic University of Norway)

Frediani, Luca (UiT The Arctic University of Norway)

UiT The Arctic University of Norway

2020-02-25

ORCA

MRChem

Python

262695

nn4654k

nn9330k

DataverseNO

UiT The Arctic University of Norway

Frediani, Luca

Brakestad, Anders

2020-02-05

https://doi.org/10.18710/KLQVOK

Chemistry, Physics, multiwavelet, static polarizability, density functional theory, benchmark, quantum chemistry, computational chemistry

<h3>Introduction</h3> This Dataverse entry contains replication data for our journal article “Static polarizabilities at the basis set limit: A benchmark of 124 species” published in Journal of Chemical Theory and Computation. It contains highly precise static polarizabilities computed in multiwavelet basis in combination with density functional theory (DFT, PBE functional). In addition, the d/Preliminaryata set contains analysis tools (Jupyter Notebooks with Python3 code) for generating the figures in the journal article. <h3>How to use</h3> Because our multiwavelet data is guaranteed to be at the complete basis set limit (to within the specified limit), it is suitable as a benchmark reference in studies of static polarizabilities where the basis set convergence is important. With multiwavelets we don't have to assume that the computed property is at the basis set limit, as is the case with Gaussian type orbital (GTO) basis sets, and it is therefore possible to confirm whether the property of interest computed basis is sufficiently converged with respect to the complete basis set limit. Our benchmark reference can also be used in the development of new methodology that requires accurate training data. <h3>Running the Jupyter Notebooks</h3> The Anaconda Python distribution is usually recommended for obtaining Jupyter Notebook. It can be downloaded from here: https://www.anaconda.com/distribution/ <br> <br> The simplest way to run the notebooks is to download all files in this DataverseNO dataset. That will preserve the directory structure, which is absolutely necessary to avoid errors. Then start your Jupyter Notebook session, navigate to the data set directory, and open the desired notebook. <h3>Journal article</h3> <a href="https://doi.org/10.1021/acs.jctc.0c00128">Brakestad et al. "Static polarizabilities at the basis set limit: A benchmark of 124 species". J. Chem. Theory Comput. (2020)</a><br><br> <h3>Abstract from journal article</h3> Benchmarking molecular properties with Gaussian-type orbital (GTO) basis sets can be challenging, because one has to assume that the computed property is at the complete basis set (CBS) limit, without a robust measure of the error. Multiwavelet (MW) bases can be systematically improved with a controllable error, which eliminates the need for such assumptions. In this work, we have used MWs within Kohn–Sham density functional theory to compute static polarizabilities for a set of 92 closed-shell and 32 open-shell species. The results are compared to recent benchmark calculations employing the GTO-type aug-pc4 basis set. We observe discrepancies between GTO and MW results for several species, with open-shell systems showing the largest deviations. Based on linear response calculations, we show that these discrepancies originate from artefacts caused by the field strength, and that several polarizabilies from a previous study were contaminated by higher order responses (hyperpolarizabilities). Based on our MW benchmark results, we can affirm that aug-pc4 is able to provide results close to the CBS limit, as long as finite-difference effects can be controlled. However, we suggest that a better approach is to use MWs, which are able to yield precise finite-difference polarizabilities even with small field strengths.

benchmark data

computational chemistry data

Static Polarizabilities at the Basis Set Limit: A Benchmark of 124 Species Anders Brakestad, Stig Rune Jensen, Peter Wind, Marco D’Alessandro, Luigi Genovese, Kathrin Helen Hopmann, and Luca Frediani Journal of Chemical Theory and Computation 2020 16 (8), 4874-4882

10.1021/acs.jctc.0c00128

Static Polarizabilities at the Basis Set Limit: A Benchmark of 124 Species Anders Brakestad, Stig Rune Jensen, Peter Wind, Marco D’Alessandro, Luigi Genovese, Kathrin Helen Hopmann, and Luca Frediani Journal of Chemical Theory and Computation 2020 16 (8), 4874-4882

• Number of cases: 24

• No. of variables per record: 10

• Type of File: text/tab-separated-values

UNF:6:9O0GNYuzlSUFLdJWO9c5VQ==

• Number of cases: 40

• No. of variables per record: 10

• Type of File: text/tab-separated-values

UNF:6:4HV3TAB0oFW84K9ILMi25w==

• Number of cases: 2

• No. of variables per record: 10

• Type of File: text/tab-separated-values

UNF:6:z+DCbgU5+V7uukBD5PtYYw==

• molecule - molecule
• basis - basis
• procs - procs
• E_0 - E_0
• E_z - E_z
• mu_0 - mu_0
• mu_z - mu_z
• alpha_iso - alpha_iso
• t_fd - t_fd
• t_lr - t_lr
• molecule - molecule
• basis - basis
• procs - procs
• E_0 - E_0
• E_z - E_z
• mu_0 - mu_0
• mu_z - mu_z
• alpha_iso - alpha_iso
• t_fd - t_fd
• t_lr - t_lr
• molecule - molecule
• basis - basis
• procs - procs
• E_0 - E_0
• E_z - E_z
• mu_0 - mu_0
• mu_z - mu_z
• alpha_iso - alpha_iso
• t_fd - t_fd
• t_lr - t_lr

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Variable Format: character

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f19628 Location:

Variable Format: character

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Summary Statistics: Valid 24.0; StDev 10.192352916329698; Min. 8.0; Mean 18.666666666666668; Max. 32.0;

Variable Format: numeric

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Summary Statistics: StDev 287.4322360583514; Max. -188.3803487; Min. -751.23631843; Valid 24.0; Mean -469.8423635725

Variable Format: numeric

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Summary Statistics: Valid 24.0; Mean -469.84213940374997; Max. -188.38036205; StDev 287.43199342648916; Min. -751.23586801

Variable Format: numeric

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Summary Statistics: Valid 24.0; StDev 0.24911294107030182; Mean -0.24365500000000004; Min. -0.51134; Max. 0.0

Variable Format: numeric

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Summary Statistics: Max. 0.02674; StDev 0.236603246440295; Mean -0.204675; Valid 24.0; Min. -0.46087

Variable Format: numeric

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Summary Statistics: Max. 44.253; Min. 17.71607; StDev 13.391211982432488; Valid 24.0; Mean 30.88019

Variable Format: numeric

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f19628 Location:

Summary Statistics: StDev 284.4723439857619; Valid 24.0; Mean 172.91666666666666; Max. 1191.0; Min. 8.0

Variable Format: numeric

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Summary Statistics: StDev 451.4597077853384; Max. 1911.0; Valid 24.0; Min. 10.0; Mean 268.2916666666667;

Variable Format: numeric

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Variable Format: character

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f19639 Location:

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Summary Statistics: Min. 8.0; Mean 43.2; Valid 40.0; StDev 33.04945788763662; Max. 96.0;

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Summary Statistics: Max. -188.4720953448; Valid 40.0; Min. -751.2368397776; StDev 284.9533309825405; Mean -469.8559812844774

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Summary Statistics: StDev 284.9530646227766; Max. -188.4721012163639; Valid 40.0; Min. -751.2363890459766; Mean -469.8557410151237

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Summary Statistics: StDev 0.2393581451946003; Mean -0.23632897500000002; Min. -0.476799; Valid 40.0; Max. 2.0E-6

Variable Format: numeric

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Summary Statistics: Valid 40.0; StDev 0.226456780142935; Mean -0.19755859625; Min. -0.42515009; Max. 0.02674332

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Summary Statistics: Valid 40.0; Min. 17.804625; Mean 31.040397749999997; StDev 13.389091905641116; Max. 44.289104

Variable Format: numeric

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Summary Statistics: Mean 710.1; Min. 34.0; StDev 861.2424180009382; Max. 3932.0; Valid 40.0;

Variable Format: numeric

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Summary Statistics: Max. 27743.0; Mean 4002.399999999999; Valid 40.0; StDev 5616.234443386149; Min. 120.0

Variable Format: numeric

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Variable Format: character

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f19624 Location:

Variable Format: character

Notes: UNF:6:OzgRjECS6lkMojl3yLYjLA==

f19624 Location:

Summary Statistics: Valid 2.0; Mean 64.0; Min. 64.0; Max. 64.0; StDev 0.0

Variable Format: numeric

Notes: UNF:6:8ZFXaZlnVurEe+UrACmvnw==

f19624 Location:

Summary Statistics: Valid 2.0; Max. -188.4923916251; Min. -751.2368390249; Mean -469.86461532499993; StDev 397.9204148314749;

Variable Format: numeric

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Summary Statistics: Max. -188.49240496251608; Mean -469.8643965870607; StDev 397.92008662735986; Min. -751.2363882116052; Valid 2.0

Variable Format: numeric

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Summary Statistics: Valid 2.0; Mean -0.238272; Max. 0.0; StDev 0.33696749393376213; Min. -0.476544

Variable Format: numeric

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Summary Statistics: Mean -0.19908186; StDev 0.31936452316446295; Max. 0.02674296; Valid 2.0; Min. -0.42490668;

Variable Format: numeric

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f19624 Location:

Summary Statistics: Mean 31.038404; Valid 2.0; Min. 17.822948; Max. 44.25386; StDev 18.689477108144896

Variable Format: numeric

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Summary Statistics: Mean 1937.0; StDev 59.39696961966999; Min. 1895.0; Valid 2.0; Max. 1979.0

Variable Format: numeric

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Summary Statistics: Valid 2.0; Min. 11953.0; StDev 1748.675069874332; Max. 14426.0; Mean 13189.5;

Variable Format: numeric

Notes: UNF:6:4zi4lnScC9h9uA1bHhYBYA==

Label:

000_README.txt

text/plain

Label:

001_ALL_GEOMETRIES.xyz

Text:

File containing cartesian coordinates, charges, and spin multiplicities of all species studied. Coordinates specified in the standard XYZ file format, each species separated by a newline character.

chemical/x-xyz

Label:

correlation.ipynb

Text:

Jupyter Notebook for generating Figure 5 in journal article.

application/x-ipynb+json

Label:

cputime_vs_nel.ipynb

Text:

Jupyter Notebook for generating Figure S5 in journal article.

application/x-ipynb+json

Label:

dipole_mapping.ipynb

Text:

Jupyter Notebook for generating Figure S3 in journal article.

application/x-ipynb+json

Label:

error_vs_time.ipynb

Text:

Jupyter Notebook for generating Figure 1 in journal article.

application/x-ipynb+json

Label:

functions.py

Text:

Python functions needed in order for the Jupyter Notebooks to work properly.

text/x-python

Label:

gtofd_vs_gtolr.ipynb

Text:

Jupyter Notebook for generating Figure 4 in journal article.

application/x-ipynb+json

Label:

gtofd_vs_mwfd.ipynb

Text:

Jupyter Notebook for generating Figure 3 in journal article.

application/x-ipynb+json

Label:

gtolr_vs_mwfd.ipynb

Text:

Jupyter Notebook for generating Figure 6 in journal article.

application/x-ipynb+json

Label:

make_species_table.ipynb

application/x-ipynb+json

Label:

mem_vs_nel.ipynb

Text:

Jupyter Notebook for generating Figure S4 in journal article.

application/x-ipynb+json

Label:

mwfd_vs_mwlr_lda.ipynb

Text:

Jupyter Notebook for generating Figure S1 in journal article.

application/x-ipynb+json

Label:

mwfd_vs_mwlr_pbe.ipynb

Text:

Jupyter Notebook for generating Figure S2 in journal article.

application/x-ipynb+json

Label:

summary.ipynb

Text:

Jupyter Notebook for generating Figure 2 in journal article.

application/x-ipynb+json

Label:

dipole_mapping.yaml

application/octet-stream

Label:

gto_lr.yaml

application/octet-stream

Label:

hg_data.yaml

application/octet-stream

Label:

mem_data_lda.yaml

application/octet-stream

Label:

mem_data_pbe.yaml

application/octet-stream

Label:

mw_fd.yaml

application/octet-stream

Label:

mw_lr_lda.yaml

application/octet-stream

Label:

mw_lr_pbe.yaml

application/octet-stream

Label:

dummyfile.txt

Text:

This is a dummy file to make sure the figs dir was uploaded correctly to DataverseNO.

text/plain