A reference data set for hydrogen combustion

  • Batzner, S. et al. Se(3) equivariant graph neural networks for efficient and accurate interatomic potentials. preprint arXiv arXiv:2101.031642021.

  • Schutt, KT et al. Equivariant message passing for the prediction of tensor properties and molecular spectra. preprint arXiv arXiv:2102.031502021.

  • Qiao, Z. et al. Unite: Unitary equivariant lattice of n-body tensors with applications to quantum chemistry. preprint arXiv arXiv:2105.146552021.

  • Haghighatlari, M. et al. Newtonnet: A Newtonian messaging network for deep learning of interatomic potentials and forces. preprint arXiv arXiv:2108.029132021.

  • Haghighatlari, M., et al. Learning to make chemical predictions: the interplay between feature representation, data and machine learning methods. Chemistry6(7): 1527–1542, ISSN 2451-9294. https://doi.org/10.1016/j.chempr.2020.05.014 2020.

  • Unke, OT & Meuwly, M. PhysNet: a neural network for predicting energies, forces, dipole moments and partial charges. J. Chem. Theoretical calculation. 15(6), 3678–3693, https://doi.org/10.1021/acs.jctc.9b00181 (2019).

    CASE
    Article
    PubMed

    Google Scholar

  • LW Bertels, LB Newcomb, M. Alaghemandi, JR Green and M. Head-Gordon. Comparative analysis of the performance of the ReaxFF reactive force field on hydrogen combustion systems. J.Phys. Chem. A, 124(27), 5631–5645, ISSN 15205215, https://doi.org/10.1021/acs.jpca.0c02734 (2020).

  • Li, J., Zhao, Z., Kazakov, A. & Dryer, F. An updated comprehensive kinetic model of hydrogen combustion. International Journal of Chemical Kinetics 36566–575, https://doi.org/10.1002/kin.20026 (2004).

    CASE
    Article

    Google Scholar

  • Grambow, C., Pattanaik, L. & Green, W. Reactants, Products, and Transition States of Elementary Chemical Reactions Based on Quantum Chemistry. Scientific data 7137, https://doi.org/10.1038/s41597-020-0460-4 (2020).

    CASE
    Article
    PubMed
    PubMed Center

    Google Scholar

  • Behler, J. & Parrinello, M. Generalized neural network representation of high-dimensional potential energy surfaces. Phys. Rev. Lett. 98146401, https://doi.org/10.1103/PhysRevLett.98.146401 (2007).

    ADS
    CASE
    Article
    PubMed

    Google Scholar

  • Smith, JS, Isayev, O. & Roitberg, AE Ani-1: A scalable neural network potential with dft accuracy at force field computational cost. chemical sciences 8(4), 3192–3203 (2017).

    CASE
    Article

    Google Scholar

  • Saint John, P. et al. Quantum chemical calculations for over 200,000 species of organic radicals and 40,000 associated closed-shell molecules. Scientific data 7244, https://doi.org/10.1038/s41597-020-00588-x (2020).

    CASE
    Article

    Google Scholar

  • Margraf, J. & Reuter, K. Systematic enumeration of elementary reaction steps in surface catalysis. ACS Omega 43370–3379, https://doi.org/10.1021/acsomega.8b03200 (2019).

    CASE
    Article
    PubMed
    PubMed Center

    Google Scholar

  • Stocker, S., Csányi, G., Reuter, K. & Margraf, J. Machine learning in chemical reaction space. Nature Communication 11ten, https://doi.org/10.1038/s41467-020-19267-x (2020).

    CASE
    Article

    Google Scholar

  • Gerasimov, G. & Shatalov, O. Kinetic mechanism of combustion of hydrogen-oxygen mixtures. Journal of Engineering Physics and Thermophysics 86987–995, https://doi.org/10.1007/s10891-013-0919-7 (2013).

    ADS
    CASE
    Article

    Google Scholar

  • Simm, G. & Reiher, M. Contextual exploration of complex chemical reaction networks. Journal of Chemical Theory and Computation 1309, https://doi.org/10.1021/acs.jctc.7b00945 (2017).

    CASE
    Article

    Google Scholar

  • Ulissi, Z., Medford, A., Bligaard, T. & Nørskov, J. To address surface reaction network complexity using machine learning of scaling relationships and dft calculations. Nature Communication 814621, https://doi.org/10.1038/ncomms14621 (2017).

    ADS
    Article
    PubMed
    PubMed Center

    Google Scholar

  • Zeng, J., Cao, L., Xu, M., Zhu, T., and Zhang, J. Complex reaction processes in combustion unraveled by neural network-based molecular dynamics simulation. Nature Communication 115713, https://doi.org/10.1038/s41467-020-19497-z (2020).

    ADS
    CASE
    Article
    PubMed
    PubMed Center

    Google Scholar

  • J. Li, Z. Zhao, A. Kazakov and FL Dryer. An updated comprehensive kinetic model of hydrogen combustion. International Journal of Chemical Kinetics, 36(10), 566–575, https://doi.org/10.1002/kin.20026 2004.

  • Mardirossian, N. & Head-Gordon, M. ωB97X-V: A hybrid 10-parameter, range-separated, functional generalized gradient approximation density with non-local correlation, designed by a fittest survival strategy. Phys. Chem. Chem. Phys. 169904–9924, https://doi.org/10.1039/c3cp54374a (2014).

    CASE
    Article
    PubMed

    Google Scholar

  • Van Voorhis, T. & Head-Gordon, M. A geometric approach to direct minimization. Molecular physics 100(11), 1713–1721, https://doi.org/10.1080/00268970110103642 (2002).

    ADS
    CASE
    Article

    Google Scholar

  • Shao, Y. et al. Advances in molecular quantum chemistry contained in the q-chem 4 program. Molecular physics 113(2), 184–215, https://doi.org/10.1080/00268976.2014.952696 (2015).

    ADS
    CASE
    Article

    Google Scholar

  • Epifanovski, E. et al. Software for the frontiers of quantum chemistry: an overview of developments in the q-chem 5 package. The Journal of Chemical Physics 155(8), 084801 (2021).

    ADS
    CASE
    Article

    Google Scholar

  • Behn, A., Zimmerman, P., Bell, A., and Head-Gordon, M. Efficient exploration of reaction pathways via a freezing chain method. The Journal of Chemical Physics 135224108, https://doi.org/10.1063/1.3664901 (2011).

    ADS
    CASE
    Article
    PubMed

    Google Scholar

  • Mallikarjun Sharada, S., Zimmerman, P., Bell, A. & Head-Gordon, M. Automated Transition State Searches Without Evaluating Burlap. Journal of Chemical Theory and Computation 85166–5174, https://doi.org/10.1021/ct300659d (2012).

    CASE
    Article
    PubMed

    Google Scholar

  • Baker, J. An algorithm for locating transition states. Journal of Computational Chemistry 7385–395 (1986).

    CASE
    Article

    Google Scholar

  • T. Verstraelen et al. Iodata: A python library for reading, writing and converting computational chemistry file formats and generating input files. Journal of Computational Chemistry42 (6): 458–464, https://doi.org/10.1002/jcc.26468. onlinelibrary.wiley.com/doi/abs/10.1002/jcc.26468 2021.

  • Mardirossian, N. & Head-Gordon, M. Thirty Years of Density Functional Theory in Computational Chemistry: An Overview and Extensive Evaluation of 200 Density Functionals. Molecular physics 115(19), 2315-2372, https://doi.org/10.1080/00268976.2017.1333644 (2017).

    ADS
    CASE
    Article

    Google Scholar

  • Gorick, L. et al. A look at the Density Functional Theory Zoo with the advanced GMTKN55 database for general main group thermochemistry, kinetics and non-covalent interactions. Phys. Chem. Chem. Phys. 1932184–32215, https://doi.org/10.1039/C7CP04913G (2017).

    CASE
    Article
    PubMed

    Google Scholar

  • Guan, X. et al. Hydrogen burning using IRC, AIMD and normal modes. fig tree slice https://doi.org/10.6084/m9.figshare.19601689 (2022).

  • Kevin A. Perras