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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics
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CKineticsDB─An Extensible and FAIR Data Management Framework and Datahub for Multiscale Modeling in Heterogeneous
Siddhant M Lambor1, Sashank Kasiraju1, Dionisios G Vlachos1,2
1RAPID Manufacturing Institute, Delaware Energy Institute, University of Delaware, Newark, Delaware 19716, United States.
Journal of Chemical Information and Modeling
|July 12, 2023
Summary
Computational research data in heterogeneous catalysis is now accessible. The Chemical Kinetics Database (CKineticsDB) ensures data reproducibility and reusability for multiscale modeling workflows.
Area of Science:
- Computational chemistry
- Materials science
- Chemical engineering
Background:
- Computational research data in heterogeneous catalysis faces accessibility challenges due to logistical limitations.
- Reproducibility and reusability are key advantages of computational research, but data fragmentation hinders progress.
- Standardized data organization, provenance, and accessibility are crucial for developing integrated multiscale modeling tools.
Purpose of the Study:
- To develop a datahub, CKineticsDB, compliant with FAIR principles for managing computational research data in heterogeneous catalysis.
- To create a centralized repository that addresses the logistical limitations hindering data accessibility and reusability.
- To facilitate the integration of multiscale modeling workflows through uniform data organization and easy accessibility.
Main Methods:
- Developed CKineticsDB, a datahub utilizing a MongoDB back-end for extensibility and a referencing-based data model to minimize redundancy.
- Created a Python software program for data processing, quality evaluation, and selective data retrieval based on catalyst and simulation parameters.
- Ensured compliance with FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles for scientific data management.
Main Results:
- CKineticsDB centralizes heterogeneous catalysis data from various theoretical scales (ab initio, thermochemistry, microkinetics).
- The database evaluates data quality, retains curated simulation information, and enables accurate regeneration of published results.
- Optimized storage and selective data retrieval based on specific catalyst and simulation parameters are implemented.
Conclusions:
- CKineticsDB accelerates the development of new reaction pathways, kinetic analysis, and catalysis discovery by providing accessible, FAIR-compliant data.
- The platform supports data-driven applications and enhances the integration of multiscale modeling workflows.
- CKineticsDB overcomes data accessibility barriers, promoting reproducibility and reusability in computational catalysis research.
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