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

Research output: Contribution to journalArticlepeer-review

7 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 languageEnglish
Article number215
JournalScientific Data
Volume9
Issue number1
DOIs
StatePublished - Dec 2022
Externally publishedYes

Fingerprint

Dive into the research topics of 'A benchmark dataset for Hydrogen Combustion'. Together they form a unique fingerprint.

Cite this