Giorgio Domenichini^1*, Guido Falk von Rudorff^1*, Otto Anatole von Lilienfeld^1,2*

1 Department of Chemistry, University of Basel, 4056 Basel, Switzerland

2 Institute of Physical Chemistry and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), University of Basel, 4056 Basel, Switzerland

* Corresponding authors emails: giorgio.domenichini@unibas.ch, guidofalk.vonrudorff@unibas.ch, anatole.vonlilienfeld@unibas.ch

DOI10.24435/materialscloud:yg-r0 [version v1]

Publication date: Nov 16, 2021

How to cite this record

Giorgio Domenichini, Guido Falk von Rudorff, Otto Anatole von Lilienfeld, Effects of perturbation order and basis set on alchemical predictions within 14 electron diatomic molecules series, detailed analysis of the various sources of errors, Materials Cloud Archive 2021.191 (2021), https://doi.org/10.24435/materialscloud:yg-r0

Description

This Dataset contains Supplementary information to the article "Effects of perturbation order and basis set on alchemical predictions" by Giorgio Domenichini, Guido Falk von Rudorff and O. Anatole von Lilienfeld. Our numerical analysis of potential energy estimates, and resulting binding curves, is based on CCSD reference results, and is limited to all neutral diatomics with 14 electrons (AlH ... N2). From those data can be predicted binding energy, equilibrium distance, and vibrational frequencies of neighboring out-of-sample diatomics with near CCSD quality using perturbations up to 5th order. The data stored as Comma Separated Value files (.csv) contain all the predictions within the 14 electron diatomics series, those are referred to the sections IIIA-IIIE of the article. To every file corresponds one of the eight basis sets used. The data are stored using consistently atomic units: Bohrs for length, electrons for charge and Hartrees for energy. For a better visualization using Jupyter notebooks and Pandas library the notebook "Display_data.ipynb" already contains the code to open the data as pandas' data frames.

Materials Cloud sections using this data

Files

File name	Size	Description
def2_TZVP.csv MD5md5:3680bd910fd57af04d67adc8597dac13	654.9 KiB	def2-TZVP
Display_data.ipynb MD5md5:95dfe0c2df8ea47a35ecc01b03ceda49	81.9 KiB	For a better visualization using Jupyter notebooks and Pandas library the notebook "Display_data.ipynb" already contains the code to open the data as pandas' data frames.
def2_QZVPP.csv MD5md5:a8c645ee17343313a313a387ea94ef48	665.1 KiB	def2-QZVPP
cc_pVTZ.csv MD5md5:2089953b70ee6c1439bf38abc929c221	644.1 KiB	cc-pVTZ
aug_cc_pVQZ.csv MD5md5:654e705df800ef06d5b01415143ee77c	665.4 KiB	aug-cc-pVQZ
_631G.csv MD5md5:c7bdaa8066bd2753d1bcad87e80a5176	633.7 KiB	6-31G"
_321G.csv MD5md5:6ff25aceff9597c7a8000827d36ad3e1	629.1 KiB	3-21G
STO_6G.csv MD5md5:25c02e07da35983ca8c264300389f288	631.8 KiB	STO-6G
STO_3G.csv MD5md5:29ef5e95a0969aa80fc57cb8a8c120a3	631.5 KiB	STO-3G

License

Files and data are licensed under the terms of the following license: Creative Commons Attribution 4.0 International.
Metadata, except for email addresses, are licensed under the Creative Commons Attribution Share-Alike 4.0 International license.

External references

Keywords

quantum alchemy error estimation APDFT ERC SNSF MARVEL