A benchmark dataset for Hydrogen Combustion

2022/05/17 by Xingyi Guan, Akshaya Kumar Das, Christopher J. Stein +10 · 1 citation
Materials Science · Physics and Astronomy · Chemistry · #Machine Learning in Materials Science #Advanced Chemical Physics Studies #Crystallography and molecular interactions #Potential energy surface #Combustion #Ab initio #Benchmark (surveying) #Hydrogen #Reaction coordinate #Chemistry #Potential energy #Density functional theory #Computer science #Statistical physics #Computational chemistry #Physics #Atomic physics #Physical chemistry

paper · pdf · doi:10.1038/s41597-022-01330-5

openalex publication_date 2022/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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.

Citations

• Advances in molecular quantum chemistry contained in the Q-Chem 4 program package
• Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

Cited by

• Scramjet [wikipedia]

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