Accurate Dehydrogenation Enthalpies Dataset for Liquid Organic Hydrogen Carriers
This contribution presents a comprehensive extension of the QM9 dataset (originally at 133 K molecules) with the calculation of G4MP2 enthalpies for 9,841 molecules, featuring up to nine heavy atoms. We present QM9-LOHC, a (de)hydrogenation dataset of 10,373 reactions, including a minimum of 5.5% weight hydrogen storage capacity in line with the Department of Energy standards for Liquid Organic Hydrogen Carriers (LOHC). By utilizing the accurate quantum chemical method G4MP2 we expand the QM9 database and explore new avenues for the exploration of hydrogen storage technologies (electrochemical LOHCs, alkali metal-LOHCs, and mixtures of LOHCs). The QM9-LOHC dataset, with its focus on reactions that vary only by hydrogen saturation levels, provides a needed data resource for advancing the design and optimization of both conventional and innovative LOHC systems, and high-fidelity data for molecular discovery.
Authors
- Logan Ward (ORCID: https://orcid.org/0000-0002-1323-5939)
- Sarah N. Elliott (ORCID: https://orcid.org/0000-0001-7463-3060)
- Ian Foster (ORCID: https://orcid.org/0000-0003-2129-5269)
- Hassan Harb (ORCID: https://orcid.org/0000-0002-6016-3122)
- Larry A. Curtiss (ORCID: https://orcid.org/0000-0001-8855-8006)
- Stephen J. Klippenstein (ORCID: https://orcid.org/0000-0001-6297-9187)
- Rajeev S. Assary (ORCID: https://orcid.org/0000-0002-9571-3307)
Institutions
- Argonne National Laboratory (US)
Publication Details
- Journal
- Scientific Data
- Published
- 2025-01-29
- DOI
- https://doi.org/10.1038/s41597-025-04468-0
- Citations
- 4
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 2.07
Funders
- U.S. Department of Energy
- Office of Science
- Office of Energy Efficiency and Renewable Energy
- Laboratory Directed Research and Development
- Bioenergy Technologies Office
- Argonne National Laboratory