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Aug 21-22, 2018: Training
Aug 22-24, 2018: Tutorials & Conference
New York, NY

Reproducible quantum chemistry in Jupyter

Chris Harris (Kitware)
11:55am–12:35pm Thursday, August 23, 2018
Average rating: *****
(5.00, 1 rating)

What you'll learn

  • Explore a platform that uses Jupyter to enable reproducible quantum mechanics calculations in the browser, using a powerful data server coupled with cloud or high-performance computing resources on the server side


In silico prediction of chemical properties has seen vast improvements in both the veracity and volume of data. However, these improvements are hampered by the lack of tools to provide transparent, reproducible workflows for analysis, knowledge discovery, and visualization of the data. Kitware and Lawrence Berkeley National Laboratory have been working on using the Jupyter environment to provide such tooling and have developed a prototype platform that uses Jupyter notebooks to enable an end-to-end workflow from simulation setup and simulation submission to a high-performance computing (HPC) resource to analytics and visualization of the results.

Jupyter provides the perfect environment to enable this sort of workflow. It allows interactive analysis while preserving the data generation and analytics steps for other scientists to review and collaborate on. The development team made the decision early on to use JupyterLab for user interface (UI) components, despite it being in early alpha release at the time. JupyterLab offers all the familiarity of the classic Jupyter Notebook with a next-generation UI, providing the flexibility, power, and extensibility needed to support the rich user experience for users’ workflows.

Chris Harris offers an overview of this platform and explores the scientific use case the platform is targeting. Chris details the core components with a particular focus on JupyterLab integration and running HPC jobs from within a notebook, shares his experience working with JupyterLab to enable the project’s novel capabilities, and explains how that was coupled with 3D visualization of chemical structure. One of the unique capabilities of the platform is its ability to initiate quantum calculations on HPC resources within a Jupyter notebook through a simple Python API. The domain expert is shielded from much of the complexity associated with submission and monitoring of HPC jobs. The calculation is defined in terms that are familiar to them, for example, specifying a geometry optimization using a particular quantum theory, and the platform takes care of creating the appropriate job and submitting it to an HPC resource.

Photo of Chris Harris

Chris Harris


Chris Harris is a staff research and development engineer at Kitware. Chris has a wide range of research interests, from high-performance computing to client-side visualization of scientific datasets. Previously, Chris worked on high-performance messaging systems at IBM. He holds a master’s degree in computing and artificial intelligence from Imperial College London.