Replication Data for: Static polarizabilities at the basis set limit: A benchmark of 124 speciesdoi:10.18710/KLQVOKDataverseNO2020-03-094Brakestad, 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 speciesdoi:10.18710/KLQVOKBrakestad, AndersJensen, Stig RuneWind, PeterD'Alessandro, MarcoGenovese, LuigiHopmann, Kathrin HelenFrediani, LucaUiT The Arctic University of Norway2020-02-25Tromsø, NorwayORCAMRChemPython262695nn4654knn9330kDataverseNOUiT The Arctic University of NorwayFrediani, LucaBrakestad, Anders2020-02-05ChemistryPhysicsmultiwaveletstatic polarizabilitydensity functional theorybenchmarkquantum chemistrycomputational 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 datacomputational chemistry dataStatic 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), 4874-488210.1021/acs.jctc.0c00128Static 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), 4874-4882gto_err_time.tab2410text/tab-separated-valuesUNF:6:9O0GNYuzlSUFLdJWO9c5VQ==mw_err_time.tab4010text/tab-separated-valuesUNF:6:4HV3TAB0oFW84K9ILMi25w==ref_err_time.tab210text/tab-separated-valuesUNF:6:z+DCbgU5+V7uukBD5PtYYw==moleculeUNF:6:4zAg80jAPesrnWDjryNzcg==basisUNF:6:a8MaamRLuW4+pfhx8+MF4Q==procs16.024.010.1923529163296988.00.018.66666666666666832.0.UNF:6:/Sbj1GG98UIjye1ryIFm1w==E_0287.4322360583514-188.3803487-751.23631843-469.841066720.024.0.-469.8423635725UNF:6:7TlaVTcwcdctvfVC93+LIQ==E_z24.0-469.84213940374997.0.0-469.84083035000003-188.38036205287.43199342648916-751.23586801UNF:6:MdvBqCORwctQDvZEnGJA/A==mu_024.00.24911294107030182-0.238015-0.24365500000000004-0.511340.0.0.0UNF:6:yzEBkKJQRJpihBgTTXErug==mu_z0.0-0.198875000000000020.026740.236603246440295-0.204675.24.0-0.46087UNF:6:D9crmhgQlvJQOlr4clUTng==alpha_iso44.2530.017.7160713.39121198243248824.0.30.66854999999999630.88019UNF:6:mMt4C7zhp4xzW/tYJsVrzA==t_fd284.4723439857619.24.030.5172.916666666666661191.00.08.0UNF:6:Nhr1cZSLDMskM1ypxZ7YIA==t_lr451.45970778533841911.024.010.0268.2916666666667.60.50.0UNF:6:mcdN8b6f78rYV+9ABmVgEA==moleculeUNF:6:j4Ww2FuVV5oQJ4/N0lJhQA==basisUNF:6:tSmhuAJFsVBL0D0U7jy36w==procs32.00.08.043.240.033.0494578876366296.0.UNF:6:6SzqFZB1fg2HO7kGsJUKJQ==E_0-188.472095344840.0-751.2368397776284.95333098254050.0.-469.84124644085-469.8559812844774UNF:6:njGifhfWYdehvLlfrmsYBQ==E_z284.9530646227766-188.4721012163639-469.840995777980.0.40.0-751.2363890459766-469.8557410151237UNF:6:L9fH6PdfB/xV4IkCCe/Qtw==mu_00.2393581451946003-0.230403-0.23632897500000002-0.4767990.040.0.2.0E-6UNF:6:Je1TSpOi/QkMHsB9eE5mnw==mu_z0.040.0-0.192829630.226456780142935-0.19755859625.-0.425150090.02674332UNF:6:qlzawyc26NbViDINDFaAlg==alpha_iso40.0.17.80462531.04039774999999731.0377760.013.38909190564111644.289104UNF:6:efCbJb8ziu1aaiRzBfj/MQ==t_fd710.1394.534.0861.24241800093820.03932.040.0.UNF:6:biCUTzdKcg33k6zZNp8Ylw==t_lr27743.01783.04002.399999999999.0.040.05616.234443386149120.0UNF:6:Ej16QChhSTDJbbwIkEKfRw==moleculeUNF:6:Z3MYJY2zPcuy/xEwTCBAXA==basisUNF:6:OzgRjECS6lkMojl3yLYjLA==procs0.0.64.02.064.064.064.00.0UNF:6:8ZFXaZlnVurEe+UrACmvnw==E_0.2.0-469.864615325-188.4923916251-751.2368390249-469.86461532499993397.92041483147490.0UNF:6:Qf9agVXFXK6iNtz9oT+1yg==E_z-188.49240496251608-469.8643965870607397.920086627359860.0-751.2363882116052-469.86439658706064.2.0UNF:6:foyFfVLAX5NvXRWfBNdcjg==mu_02.00.0-0.2382720.0.0.33696749393376213-0.238272-0.476544UNF:6:vsjihv/NczRmpUdtqN2/hw==mu_z0.0-0.199081860.319364523164462950.026742962.0-0.42490668.-0.19908186UNF:6:Suhh5dnfKyIkUIsChw88yQ==alpha_iso31.038404.31.0384040.02.017.82294844.2538618.689477108144896UNF:6:1NWGXDTgBchnh8VysmB4MA==t_fd1937.059.396969619669991895.00.01937.0.2.01979.0UNF:6:w43nHYZ2l3PWdmDj1vYl+w==t_lr2.013189.511953.01748.67506987433214426.00.013189.5.UNF:6:4zi4lnScC9h9uA1bHhYBYA==000_README.txttext/plain001_ALL_GEOMETRIES.xyzFile 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-xyzcorrelation.ipynbJupyter Notebook for generating Figure 5 in journal article.application/x-ipynb+jsoncputime_vs_nel.ipynbJupyter Notebook for generating Figure S5 in journal article.application/x-ipynb+jsondipole_mapping.ipynbJupyter Notebook for generating Figure S3 in journal article.application/x-ipynb+jsonerror_vs_time.ipynbJupyter Notebook for generating Figure 1 in journal article.application/x-ipynb+jsonfunctions.pyPython functions needed in order for the Jupyter Notebooks to work properly.text/x-pythongtofd_vs_gtolr.ipynbJupyter Notebook for generating Figure 4 in journal article.application/x-ipynb+jsongtofd_vs_mwfd.ipynbJupyter Notebook for generating Figure 3 in journal article.application/x-ipynb+jsongtolr_vs_mwfd.ipynbJupyter Notebook for generating Figure 6 in journal article.application/x-ipynb+jsonmake_species_table.ipynbapplication/x-ipynb+jsonmem_vs_nel.ipynbJupyter Notebook for generating Figure S4 in journal article.application/x-ipynb+jsonmwfd_vs_mwlr_lda.ipynbJupyter Notebook for generating Figure S1 in journal article.application/x-ipynb+jsonmwfd_vs_mwlr_pbe.ipynbJupyter Notebook for generating Figure S2 in journal article.application/x-ipynb+jsonsummary.ipynbJupyter Notebook for generating Figure 2 in journal article.application/x-ipynb+jsondipole_mapping.yamlapplication/octet-streamgto_lr.yamlapplication/octet-streamhg_data.yamlapplication/octet-streammem_data_lda.yamlapplication/octet-streammem_data_pbe.yamlapplication/octet-streammw_fd.yamlapplication/octet-streammw_lr_lda.yamlapplication/octet-streammw_lr_pbe.yamlapplication/octet-streamdummyfile.txtThis is a dummy file to make sure the figs dir was uploaded correctly to DataverseNO.text/plain