Literature DB >> 33368350

IOData: A python library for reading, writing, and converting computational chemistry file formats and generating input files.

Toon Verstraelen1, William Adams2, Leila Pujal3, Alireza Tehrani3, Braden D Kelly2, Luis Macaya4, Fanwang Meng2, Michael Richer2, Raymundo Hernández-Esparza2, Xiaotian Derrick Yang2,5, Matthew Chan2, Taewon David Kim2, Maarten Cools-Ceuppens1, Valerii Chuiko2,6, Esteban Vöhringer-Martinez4, Paul W Ayers2, Farnaz Heidar-Zadeh3.   

Abstract

IOData is a free and open-source Python library for parsing, storing, and converting various file formats commonly used by quantum chemistry, molecular dynamics, and plane-wave density-functional-theory software programs. In addition, IOData supports a flexible framework for generating input files for various software packages. While designed and released for stand-alone use, its original purpose was to facilitate the interoperability of various modules in the HORTON and ChemTools software packages with external (third-party) molecular quantum chemistry and solid-state density-functional-theory packages. IOData is designed to be easy to use, maintain, and extend; this is why we wrote IOData in Python and adopted many principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. This article is the official release note of the IOData library.
© 2020 Wiley Periodicals LLC.

Keywords:  JSON schema; basis set conversion; chemistry software development; computational chemistry; data parsing; file format conversion; input file generation; molecular mechanics; quantum chemistry; theoretical chemistry Python library

Year:  2020        PMID: 33368350     DOI: 10.1002/jcc.26468

Source DB:  PubMed          Journal:  J Comput Chem        ISSN: 0192-8651            Impact factor:   3.376


  2 in total

1.  A benchmark dataset for Hydrogen Combustion.

Authors:  Xingyi Guan; Akshaya Das; Christopher J Stein; Farnaz Heidar-Zadeh; Luke Bertels; Meili Liu; Mojtaba Haghighatlari; Jie Li; Oufan Zhang; Hongxia Hao; Itai Leven; Martin Head-Gordon; Teresa Head-Gordon
Journal:  Sci Data       Date:  2022-05-17       Impact factor: 8.501

2.  Organic materials repurposing, a data set for theoretical predictions of new applications for existing compounds.

Authors:  Ömer H Omar; Tahereh Nematiaram; Alessandro Troisi; Daniele Padula
Journal:  Sci Data       Date:  2022-02-14       Impact factor: 6.444

  2 in total

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