This paper introduces FireWorks, a workflow software for running high-throughput calculation workflows atsupercomputing centers. FireWorks has been used to complete over 50 million CPU-hours worth of compu-tational chemistry and materials science calculations at the National Energy Research Supercomputing Center.It has been designed to serve the demanding high-throughput computing needs of these applications, with ex-tensive support for (i) concurrent execution through job packing, (ii) failure detection and correction, (iii) prov-enance and reporting for long-running projects, (iv) automated duplicate detection, and (v) dynamic workflows(i.e., modifying the workflow graph during runtime). We have found that these features are highly relevant toenabling modern data-driven and high-throughput science applications, and we discuss our implementationstrategy that rests on Python and NoSQL databases (MongoDB). Finally, we present performance data andlimitations of our approach along with planned future work. Copyright © 2015 John Wiley & Sons, Ltd.