(443a) An Automated Job Management Framework for High Fidelity Quantum Chemistry Calculations
AIChE Annual Meeting
2019
2019 AIChE Annual Meeting
Topical Conference: Applications of Data Science to Molecules and Materials
Applications of Data Science in Catalysis and Reaction Engineering II
Wednesday, November 13, 2019 - 8:00am to 8:18am
In this talk, we present an automated framework that automatically spawns jobs, and handles dependencies between jobs, on multiple supercomputing clusters, with minimal user interaction. Specifically, we use FireWorks [1], an open source automated framework written in Python for defining, managing, and executing complex workflows. Jobs along with their dependencies are stored in MongoDB. FireWorks consists of two components, a FireServer that manages workflows and a FireWorker that executes these workflows. We demonstrate the efficacy of FireWorks through automated scheduling of high-fidelity quantum chemistry calculations for the construction of detailed kinetic models. This will enable accurate thermodynamic and kinetic parameters to be calculated in a high-throughput manner that makes the most of computational resources available to a user. In addition, this framework allows for the creation of a standardized dataset that can be used as training data for machine-learning based estimation methods.
References
1. Jain, A., Ong, S. P., Chen, W., Medasani, B., Qu, X., Kocher, M., Brafman, M., Petretto, G., Rignanese, G.-M., Hautier, G., Gunter, D., and Persson, K. A. (2015) FireWorks: a dynamic workflow system designed for high-throughput applications. Concurrency Computat.: Pract. Exper., 27: 5037â5059. doi: 10.1002/cpe.3505.