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Title: A benchmark dataset for Hydrogen Combustion

Journal Article · · Scientific Data
 [1];  [2];  [3];  [4];  [2];  [5]; ORCiD logo [2];  [2];  [2];  [1];  [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Univ. of California, Berkeley, CA (United States)
  3. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of Duisburg-Essen (Germany)
  4. Univ. of California, Berkeley, CA (United States); Queen’s Univ., Ontario (Canada)
  5. Univ. of California, Berkeley, CA (United States); Beijing Normal Univ. (China)
+ Show Author Affiliations

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.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
National Science Foundation; USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1878744
Journal Information:
Scientific Data, Journal Name: Scientific Data Journal Issue: 1 Vol. 9; ISSN 2052-4463
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

References (32)

Hydrogen Combustion using IRC, AIMD and normal modes dataset January 2022
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Context-Driven Exploration of Complex Chemical Reaction Networks journal November 2017
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges journal April 2019
Benchmarking the Performance of the ReaxFF Reactive Force Field on Hydrogen Combustion Systems journal June 2020
Systematic Enumeration of Elementary Reaction Steps in Surface Catalysis journal February 2019
Automated Transition State Searches without Evaluating the Hessian journal September 2012
To address surface reaction network complexity using scaling relations machine learning and DFT calculations journal March 2017
Machine learning in chemical reaction space journal October 2020
Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation journal November 2020
Quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules journal July 2020
Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry journal May 2020
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost journal January 2017
A look at the density functional theory zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and noncovalent interactions journal January 2017
NewtonNet: A Newtonian message passing network for deep learning of interatomic potentials and forces journal January 2022
ωB97X-V: A 10-parameter, range-separated hybrid, generalized gradient approximation density functional with nonlocal correlation, designed by a survival-of-the-fittest strategy journal January 2014
Efficient exploration of reaction paths via a freezing string method journal December 2011
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Reactants, products, and transition states of elementary chemical reactions based on quantum chemistry dataset January 2020
Hydrogen Combustion using IRC, AIMD and normal modes dataset January 2022
Hydrogen Combustion using IRC, AIMD and normal modes dataset January 2022
Hydrogen Combustion using IRC, AIMD and normal modes dataset January 2022
Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals [Supplemental Data] fileset June 2017
Quantum chemical calculations for over 200,000 organic radical species and 40,000 associated closed-shell molecules. collection January 2020

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