The OpenKIM processing pipeline: A cloud-based automatic material property computation engine

D. S. Karls, M. Bierbaum, A. A. Alemi, R. S. Elliott, J. P. Sethna, E. B. Tadmor

Research output: Contribution to journalArticlepeer-review

Abstract

The Open Knowledgebase of Interatomic Models (OpenKIM) is a framework intended to facilitate access to standardized implementations of interatomic models for molecular simulations along with computational protocols to evaluate them. These protocols include tests to compute material properties predicted by models and verification checks to assess their coding integrity. While housing this content in a unified, publicly available environment constitutes a major step forward for the molecular modeling community, it further presents the opportunity to understand the range of validity of interatomic models and their suitability for specific target applications. To this end, OpenKIM includes a computational pipeline that runs tests and verification checks using all available interatomic models contained within the OpenKIM Repository at https://openkim.org. The OpenKIM Processing Pipeline is built on a set of Docker images hosted on distributed, heterogeneous hardware and utilizes open-source software to automatically run test-model and verification check-model pairs and resolve dependencies between them. The design philosophy and implementation choices made in the development of the pipeline are discussed as well as an example of its application to interatomic model selection.

Original languageEnglish (US)
Article number0014267
JournalJournal of Chemical Physics
Volume153
Issue number6
DOIs
StatePublished - Aug 14 2020

Bibliographical note

Funding Information:
This research was partly supported by the National Science Foundation (NSF) under Grant Nos. DMR-1834251 and DMR-1834332. The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the results reported in this paper. The authors thank Ronald Miller, Noam Bernstein, Mingjian Wen, and Yaser Afshar for helpful discussions and for contributing to this effort.

Publisher Copyright:
© 2020 Author(s).

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