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

Sci Data. 2022 May 17;9(1):215. doi: 10.1038/s41597-022-01330-5.

Authors

Xingyi Guan^{1

2}, Akshaya Das¹, Christopher J Stein^{1

2

3}, Farnaz Heidar-Zadeh^{1

4}, Luke Bertels¹, Meili Liu^{1

5}, Mojtaba Haghighatlari¹, Jie Li¹, Oufan Zhang¹, Hongxia Hao^{1

2}, Itai Leven^{1

2}, Martin Head-Gordon^{1

2}, Teresa Head-Gordon^{6

7

8}

Affiliations

¹ Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California, Berkeley, CA, USA.
² Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
³ Theoretical Physics and Center for Nanointegration Duisburg-Essen (CENIDE), University of Duisburg-Essen, 47048, Duisburg, Germany.
⁴ Department of Chemistry, Queen's University, Kingston, Ontario, K7L 3N6, Canada.
⁵ Department of Chemistry, Beijing Normal University, Beijing, 100875, China.
⁶ Kenneth S. Pitzer Theory Center and Department of Chemistry, University of California, Berkeley, CA, USA. thg@berkeley.edu.
⁷ Chemical Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA. thg@berkeley.edu.
⁸ Departments of Bioengineering and Chemical and Biomolecular Engineering, University of California, Berkeley, CA, USA. thg@berkeley.edu.

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.

Abstract

Grants and funding