QCArchive Overview#


QCArchive is a platform that makes running large numbers of quantum chemistry calculations in a robust and scalable manner accessible to computational chemists. QCArchive is designed to handle thousands to millions of computations, storing them in a database for later sharing, retrieval and analysis, or export.


There is a growing need for enormous amounts of high-accuracy data for a variety of purposes, including AI/ML and method development. However, while running a few computations is relatively straightforward, as the number of computations required increases, the complexity of managing and running them increases dramatically.

This complexity increase is due to a variety of factors, but is primarily driven by the bespoke nature of running different QM codes, the coordination of distributed computation resources, as well as handling of errors that inevitably appear as the number of computations scale up.

While there are a variety of distributed computation platforms available, they tend to be very general and require a large amount of customization by chemists to run quantum chemistry calculations, whereas QCArchive itself is build around the concepts that computational chemists will find familiar. In reality, QCArchive utilizes some of these tools (such as Parsl) to achieve its goals.

The overarching goal of the QCArchive project is to make creating and managing these kinds of datasets as easy as possible for chemists who know some Python, but are unfamiliar with distributed computing at this scale.

Overall Architecture#


QCArchive contains three main components. Central to the architecture is server software (QCFractal) that is responsible for storing and managing data. The server is accessed by a client (QCPortal) that provides a Python-native way to submit and manage calculations.

A third component is the workers (QCFractalCompute). This is responsible for running the calculations.

In a typical setup, the server is run on a dedicated machine, while the client is run on a user’s machine. The workers are set up on a supercomputing cluster, with a process on the head node responsible for coordinating job submission and monitoring (although other setups are possible).


QCPortal is not the only way to access the server. The server exposes a JSON-based web API, and so any software written in any language that can make requests to these kinds of APIs can be used.

If you are just interested in connecting to a server and running computations, you only need the client on your local computer. For that information, see the QCArchive User Guide.

For information about setting up a server and/or workers see QCFractal Server User Guide.

Source code#

The source code is available on github at MolSSI/QCFractal.git. Packages are also made available from PyPI and conda-forge (see QCPortal Installation & Setup and QCFractal Installation and Setup).