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Open AccessOpen Access JUSTC Chemistry 15 July 2024

Machine learning molecular dynamics simulations of liquid methanol

  • Hefei National Research Center for Physical Sciences at the Microscale, Department of Chemical Physics, University of Science and Technology of China, Hefei 230026, China

Cite this:
https://doi.org/10.52396/JUSTC-2024-0031
More Information
  • Author Bio:

    Jie Qian received his B.S. degree in Chemistry from the University of Science and Technology of China (USTC) in 2020 and is working toward a master’s degree at USTC. His research interests include machine learning force fields and relevant applications in condensed phase systems

    Bin Jiang is a Professor of Chemistry at the University of Science and Technology of China (USTC). He received his B.S. and Ph.D. degrees from Nanjing University and performed postdoctoral research at the University of New Mexico. He joined the USTC in 2015. His research interests focus on machine learning method development and applications to potential energy surfaces across gas-phase and gas-surface interfaces, as well as quantum/classical dynamics of gas-surface reactions

  • Corresponding author: E-mail: bjiangch@ustc.edu.cn
  • Received Date: 27 February 2024
  • Accepted Date: 01 April 2024
    • Available Online: 15 July 2024
    Abstract

  • Abstract

    As the simplest hydrogen-bonded alcohol, liquid methanol has attracted intensive experimental and theoretical interest. However, theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals. Inspired by recent studies on bulk water using increasingly accurate machine learning force fields, we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction. Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations, yielding the radial distribution functions, self-diffusion coefficients, and hydrogen bond network properties with very small statistical errors. The resulting structural and dynamical properties are compared well with the experimental data, demonstrating the superior accuracy of this machine learning force field. This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.

    Graphical abstract

    Simulated liquid methanol by machine learning force field.

    Abstract

    As the simplest hydrogen-bonded alcohol, liquid methanol has attracted intensive experimental and theoretical interest. However, theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals. Inspired by recent studies on bulk water using increasingly accurate machine learning force fields, we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction. Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations, yielding the radial distribution functions, self-diffusion coefficients, and hydrogen bond network properties with very small statistical errors. The resulting structural and dynamical properties are compared well with the experimental data, demonstrating the superior accuracy of this machine learning force field. This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.

    • Public Summary

    • We develop a machine learning force field for liquid methanol at the level of hybrid functional revPBE0 plus dispersion correction.
    • Machine learning molecular dynamics simulations are orders of magnitude faster than ab initio molecular dynamics simulations.
    • Our machine learning force field predicts the radial distribution functions, self-diffusion coefficients and hydrogen bonding features in reasonably good agreement with the experimental data.

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    • References(84)
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    Figure  1.  (a) A snapshot of the simulation cell of liquid methanol containing 32 CH3OH molecules. (b) Potential energies and (c) forces obtained from the comparison of the EANN potential and DFT results. The energy zero is defined as the mean potential energy of the AIMD trajectory.

    Figure  2.  Comparison of the calculated intermolecular radial distribution functions of (a) O–O, (b) O–H, (c) H–H, (d) C–C, (e) C–O, and (f) C–H in bulk methanol from current AIMD and MLMD simulations at the revPBE0-D3 level with the experimental data[3,4] at room temperature, which are taken from neutron diffraction results fitted by the empirical potential structure refinement (EPSR) computer simulation. Additionally, previous AIMD results at the BLYP-D3 and B97-D2 levels for deuterated methanol and mbGDML results at the MP2 level whenever available are shown.

    Figure  3.  A log-log plot of the MSD with respect to the correlation time of the O atom and the centroid of methanol was used to identify the “middle” region. The blue dashed line indicates a slope of 1 (target diffusive regime).

    Figure  4.  (a) Definition of the geometric criteria for H-bonds in a methanol dimer. (b) H-bond density probability distribution as a function of $ \angle {\text{HO}} \cdots {\text{O}} $ and $ {r}_{\mathrm{H}\mathrm{O}\cdots \mathrm{H}} $ obtained from all trajectories. The peak position is marked in red.

    Figure  5.  (a) Snapshot of one chain of methanol molecules illustrating multiple types of H-bonds (blue dashed lines). (b) Scatter plot and fitted biexponential function curve of the H-bond autocorrelation obtained from MLMD.

    [1]
    Alias M S, Kamarudin S K, Zainoodin A M, et al. Active direct methanol fuel cell: An overview. Int. J. Hydrog. Energy, 2020, 45 (38): 19620–19641. doi: 10.1016/j.ijhydene.2020.04.202
    [2]
    Olabi A G, Onumaegbu C, Wilberforce T, et al. Critical review of energy storage systems. Energy, 2021, 214: 118987. doi: 10.1016/j.energy.2020.118987
    [3]
    Yamaguchi T, Hidaka K, Soper A K. The structure of liquid methanol revisited: a neutron diffraction experiment at −80 °C and +25 °C. Mol. Phys, 1999, 96 (8): 1159–1168. doi: 10.1080/00268979909483060
    [4]
    Yamaguchi T, Hidaka K, Soper A K. ERRATUM: The structure of liquid methanol revisited: a neutron diffraction experiment at −80 °C and +25 °C. Mol. Phys, 1999, 97 (4): 603–605. doi: 10.1080/00268979909482859
    [5]
    Adya A K, Bianchi L, Wormald C J. The structure of liquid methanol by H/D substitution technique of neutron diffraction. J. Chem. Phys., 2000, 112 (9): 4231–4241. doi: 10.1063/1.480969
    [6]
    Narten A H, Habenschuss A. Hydrogen bonding in liquid methanol and ethanol determined by x-ray diffraction. J. Chem. Phys., 1984, 80 (7): 3387–3391. doi: 10.1063/1.447093
    [7]
    Falk M, Whalley E. Infrared spectra of methanol and deuterated methanols in gas, liquid, and solid phases. J. Chem. Phys., 1961, 34 (5): 1554–1568. doi: 10.1063/1.1701044
    [8]
    Lin K, Zhou X G, Luo Y, et al. The microscopic structure of liquid methanol from Raman spectroscopy. J. Phys. Chem. B, 2010, 114 (10): 3567–3573. doi: 10.1021/jp9121968
    [9]
    Gaffney K J, Davis P H, Piletic I R, et al. Hydrogen bond dissociation and reformation in methanol oligomers following hydroxyl stretch relaxation. J. Phys. Chem. A, 2002, 106 (50): 12012–12023. doi: 10.1021/jp021696g
    [10]
    Shinokita K, Cunha A V, Jansen T L C, et al. Hydrogen bond dynamics in bulk alcohols. J. Chem. Phys., 2015, 142 (21): 212450. doi: 10.1063/1.4921574
    [11]
    Salamatova E, Cunha A V, Shinokita K, et al. Hydrogen bond and lifetime dynamics in diluted alcohols. Phys. Chem. Chem. Phys., 2017, 19 (41): 27960–27967. doi: 10.1039/C7CP03222F
    [12]
    Jorgensen W L. Optimized intermolecular potential functions for liquid alcohols. J. Phys. Chem., 1986, 90 (7): 1276–1284. doi: 10.1021/j100398a015
    [13]
    Haughney M, Ferrario M, McDonald I R. Pair interactions and hydrogen-bond networks in models of liquid methanol. Mol. Phys., 1986, 58 (4): 849–853. doi: 10.1080/00268978600101611
    [14]
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