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WIREs Comput Mol Sci

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The MolSSI QCArchive project: An open‐source platform to compute, organize, and share quantum chemistry data

Advanced Review

Daniel G. A. Smith, Doaa Altarawy, Lori A. Burns, Matthew Welborn, Levi N. Naden, Logan Ward, Sam Ellis, Benjamin P. Pritchard, T. Daniel Crawford

Published Online: Jul 31 2020

DOI: 10.1002/wcms.1491

Full article on Wiley Online Library: HTML PDF

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Abstract The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python‐based software infrastructure that powers automated computation and storage of quantum chemistry (QC) results. The MolSSI‐hosted central server provides the computational molecular sciences community a location to freely access tens of millions of QC computations for machine learning, methodology assessment, force‐field fitting, and more through a Python interface. Facile, user‐friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at https://qcarchive.molssi.org. The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of QC computations for individuals or groups of researchers at any scale. The QCArchive Infrastructure is open‐source (BSD‐3C), code repositories can be found at https://github.com/MolSSI, and releases can be downloaded via PyPI and Conda. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry Data Science > Computer Algorithms and Programming

Relationship between the QCArchive project, MolSSI, and the CMS community

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An example of the Reaction Datasets Viewer app showing the S22 dataset under several DFT method and basis combinations

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An example of the Quantum Chemistry Machine Learning Datasets Repository showing an expansion of the COMP6 DrugBank dataset

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Visualization of the butane carbon backbone torsion using the TorsionDrive Dataset object to compare several methods

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Setup and evaluation of a force field, machine learning potential, and DFT computations with a TorsionDrive Dataset object

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An example setup for a new TorsionDrive Dataset object with hydrogen peroxide and butane molecules

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An image showing QCPortal pulling the S22 dataset from the MQCAS, listing all current B3LYP interaction energies, and rendering an example molecular complex from the dataset

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The full QCArchive Infrastructure and how each layer and module communicates. Between different locations, all pieces communicate over TCP/IP protocols; within individual boxes, all pieces are connected through a single Python interpreter; and between a resource task queue and workers, communication can come in a variety of RPC sockets over either MPI or local Ethernet

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A Jupyter notebook demonstrating pulling a water geometry from the PubChem database⁵² and computing both Q‐Chem and Psi4 with the same input. Note the difference in absolute energies is primarily due to the fact that Q‐Chem uses direct SCF algorithms by default while Psi4 uses density‐fitting

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A Jupyter notebook showing a molecule object created from an XYZ string, the O‐H distance (Bohr) and HOH angle (degrees) measured, and the computation of a conversion factor for a gradient

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