GaussDal: An open source database management system for quantum chemical computations

Alsberg, Bjørn K.; Bjerke, Håvard; Navestad, Gunn M.; Åstrand, Per‐Olof

doi:10.1016/j.cpc.2005.04.008

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…5 This has been developed with workflows in mind and in particular it has been incorporated into the SciCraft visual programming environment. 6 cclib differs from related work in that it does not rely on an additional file format or database, and is aimed at users who are developing algorithms or simply need to extract data from output files, although enabling workflows is a possible application.…”

Section: Related Softwarementioning

confidence: 99%

cclib: A library for package‐independent computational chemistry algorithms

2007

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There are now a wide variety of packages for electronic structure calculations, each of which differs in the algorithms implemented and the output format. Many computational chemistry algorithms are only available to users of a particular package despite being generally applicable to the results of calculations by any package. Here we present cclib, a platform for the development of package-independent computational chemistry algorithms. Files from several versions of multiple electronic structure packages are automatically detected, parsed, and the extracted information converted to a standard internal representation. A number of population analysis algorithms have been implemented as a proof of principle. In addition, cclib is currently used as an input filter for two GUI applications that analyze output files: PyMOlyze and GaussSum.

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Section: Related Softwarementioning

confidence: 99%

cclib: A library for package‐independent computational chemistry algorithms

2007

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“…While there are several software projects that could be helpful in this task, such as spreadsheet automation, desktop graphical interfaces for results analysis (GaussView, GaussSum), or custom database creation (GaussDal, MyChem), they mainly operate as standalone data collectors. In the end, yet another system would be needed to automate the full pipeline, be it a custom script or a multipurpose workflow editor such as KNIME-CDK or Taverna .…”

Section: Introductionmentioning

confidence: 99%

ESIgen: Electronic Supporting Information Generator for Computational Chemistry Publications

Pedregal

Gómez-Orellana

Maréchal

2018

J. Chem. Inf. Model.

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Electronic supporting information (ESI) occupies a fundamental position in the way scientists report their work. It is a key element in lightening the writing of the core manuscript and makes concise communication easier for the authors. Computational chemistry, as all fields related to structural studies of molecules, tends to generate huge amounts of data that should be inserted in the ESI. ESI reports originating from computational chemistry works generally reach tens of sheets long and include 3D depictions, coordinates, energies, and other characteristics of the structures involved in the molecular process understudy. While most experienced users end up building scripts that dig throughout the output files searching for the relevant data, this is not the case for users without programming experience or time. Here we present an automated ESI generator supported by both web-based and command line interfaces. Focused on quantum mechanics calculations outputs so far, we trust that the community would find this tool useful. Source code is freely available at https://github.com/insilichem/esigen . A web app public demo can be found at http://esi.insilichem.com .

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“…Another notable work is that of Alsberg [42,43], which is unique in the literature by employing data mining on electronic structure descriptions. In that work, he analyzed the charge density of polymers via data mining tools trying to capture some trend connecting the chemistry with the electronic structure.…”

Section: Novelty Of Workmentioning

confidence: 99%

Statistical learning for alloy design from electronic structure calculations

Broderick

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APPENDIX: BACKGROUND INFORMATION 99 iv A.1 Mathematics of PCA 99 A.2 Mathematics of PLS 102 A.3 Schematic of Modeling the Density of States 104 A.4 Mathematics of Haydock's Recursion Method 108 REFERENCES 110 v ACKNOWLEDGMENTS I would like to express my deepest gratitude to my thesis advisor, Professor Krishna Rajan. He has provided me with numerous opportunities beyond what is normal for a graduate student, and his guidance has made this experience very productive. I would also like to thank my thesis committee members-Professors Vasant Honavar, Michael Kessler, Richard LeSar and Sriram Sundrarajan-for their insights. I have been fortunate to have collaborated with numerous excellent scientists. I would like to specifically mention Professor Shuichi Iwata and his laboratory members at the University of Tokyo for their hospitality during my visits and for their insights and ideas. I would also like to thank Professor Hafid Aourag at the Univeristy Abou Bekr Belkaid in Algeria for providing me numerous ideas and suggestions during the course of this work. He has been instrumental in the development of the work discussed in this thesis. I would also like to acknowledge my fellow group members for the numerous discussions we have had over the years. In particular, I would like to thank Dr. Changwon Suh for his advice and teachings during my beginning years in graduate school.

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GaussDal: An open source database management system for quantum chemical computations

Cited by 5 publications

References 14 publications

cclib: A library for package‐independent computational chemistry algorithms

cclib: A library for package‐independent computational chemistry algorithms

ESIgen: Electronic Supporting Information Generator for Computational Chemistry Publications

Statistical learning for alloy design from electronic structure calculations

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