A benchmark dataset for Hydrogen Combustion

Xingyi Guan; Akshaya Das; Christopher J. Stein; Farnaz Heidar-Zadeh; Luke Bertels; Meili Liu; Mojtaba Haghighatlari; Jie Li; Oufan Zhang; Hongxia Hao; Itai Leven; Martin Head-Gordon; Teresa Head-Gordon

doi:10.1038/s41597-022-01330-5

A benchmark dataset for Hydrogen Combustion

Xingyi Guan, Akshaya Das, Christopher J. Stein, Farnaz Heidar-Zadeh, Luke Bertels, Meili Liu, Mojtaba Haghighatlari, Jie Li, Oufan Zhang, Hongxia Hao, Itai Leven, Martin Head-Gordon, Teresa Head-Gordon

Quantum Computational Science

Research output: Contribution to journal › Article › peer-review

13 Scopus citations

Abstract

The generation of reference data for deep learning models is challenging for reactive systems, and more so for combustion reactions due to the extreme conditions that create radical species and alternative spin states during the combustion process. Here, we extend intrinsic reaction coordinate (IRC) calculations with ab initio MD simulations and normal mode displacement calculations to more extensively cover the potential energy surface for 19 reaction channels for hydrogen combustion. A total of ∼290,000 potential energies and ∼1,270,000 nuclear force vectors are evaluated with a high quality range-separated hybrid density functional, ωB97X-V, to construct the reference data set, including transition state ensembles, for the deep learning models to study hydrogen combustion reaction.

Original language	English
Article number	215
Journal	Scientific Data
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41597-022-01330-5
State	Published - Dec 2022

Funding

We thank the National Science Foundation under grant CHE-1955643. F.H-Z. acknowledges financial support from Natural Sciences and Engineering Research Council (NSERC) of Canada. M. Liu thanks the China Scholarship Council for a visiting scholar fellowship. C.J.S. acknowledges funding by the Ministry of Innovation, Science and Research of North Rhine-Westphalia (\u201CNRW R\u00FCckkehrerprogramm\u201D) and an Early Postdoc Mobility fellowship from the Swiss National Science Foundation. This research used computational resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

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abstract = "The generation of reference data for deep learning models is challenging for reactive systems, and more so for combustion reactions due to the extreme conditions that create radical species and alternative spin states during the combustion process. Here, we extend intrinsic reaction coordinate (IRC) calculations with ab initio MD simulations and normal mode displacement calculations to more extensively cover the potential energy surface for 19 reaction channels for hydrogen combustion. A total of ∼290,000 potential energies and ∼1,270,000 nuclear force vectors are evaluated with a high quality range-separated hybrid density functional, ωB97X-V, to construct the reference data set, including transition state ensembles, for the deep learning models to study hydrogen combustion reaction.",

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AU - Das, Akshaya

AU - Stein, Christopher J.

AU - Heidar-Zadeh, Farnaz

AU - Bertels, Luke

AU - Liu, Meili

AU - Haghighatlari, Mojtaba

AU - Li, Jie

AU - Zhang, Oufan

AU - Hao, Hongxia

AU - Leven, Itai

AU - Head-Gordon, Martin

AU - Head-Gordon, Teresa

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A benchmark dataset for Hydrogen Combustion

Abstract

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