ISSN 0253-2778

CN 34-1054/N

open
Free to Publish. Free to Read.
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.

    FullText(HTML)
  • loading
    • References(84)
  • [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]
    van Leeuwen M E, Smit B. Molecular simulation of the vapor-liquid coexistence curve of methanol. J. Phys. Chem., 1995, 99 (7): 1831–1833. doi: 10.1021/j100007a006
    [15]
    Schnabel T, Srivastava A, Vrabec J, et al. Hydrogen bonding of methanol in supercritical CO2: comparison between 1H NMR spectroscopic data and molecular simulation results. J. Phys. Chem. B, 2007, 111 (33): 9871–9878. doi: 10.1021/jp0720338
    [16]
    Guevara-Carrion G, Nieto-Draghi C, Vrabec J, et al. Prediction of transport properties by molecular simulation: methanol and ethanol and their mixture. J. Phys. Chem. B, 2008, 112 (51): 16664–16674. doi: 10.1021/jp805584d
    [17]
    Jorgensen W L, Maxwell D S, Tirado-Rives J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc., 1996, 118 (45): 11225–11236. doi: 10.1021/ja9621760
    [18]
    Wang J, Wolf R M, Caldwell J W, et al. Development and testing of a general amber force field. J. Comput. Chem., 2004, 25 (9): 1157–1174. doi: 10.1002/jcc.20035
    [19]
    Haughney M, Ferrario M, McDonald I R. Molecular-dynamics simulation of liquid methanol. J. Phys. Chem., 1987, 91 (19): 4934–4940. doi: 10.1021/j100303a011
    [20]
    Gonzalez-Salgado D, Vega C. A new intermolecular potential for simulations of methanol: The OPLS/2016 model. J. Chem. Phys., 2016, 145 (3): 034508. doi: 10.1063/1.4958320
    [21]
    Watanabe T, Ohashi K. Similarity and dissimilarity between water and methanol in solvent effects on the spectroscopic properties of aniline: Molecular dynamics and time-dependent DFT studies. Comput. Theor. Chem., 2022, 1215: 113850. doi: 10.1016/j.comptc.2022.113850
    [22]
    Galicia-Andrés E, Dominguez H, Pusztai L, et al. Composition dependence of thermodynamic, dynamic and dielectric properties of water–methanol model mixtures. Molecular dynamics simulation results with the OPLS-AA model for methanol. J. Mol. Liq., 2015, 212: 70–78. doi: 10.1016/j.molliq.2015.08.061
    [23]
    Tsuchida E, Kanada Y, Tsukada M. Density-functional study of liquid methanol. Chem. Phys. Lett., 1999, 311 (3/4): 236–240. doi: 10.1016/S0009-2614(99)00851-9
    [24]
    Pagliai M, Cardini G, Righini R, et al. Hydrogen bond dynamics in liquid methanol. J. Chem. Phys., 2003, 119 (13): 6655–6662. doi: 10.1063/1.1605093
    [25]
    Handgraaf J W, van Erp T S, Meijer E J. Ab initio molecular dynamics study of liquid methanol. Chem. Phys. Lett., 2003, 367 (5/6): 617–624. doi: 10.1016/S0009-2614(02)01779-7
    [26]
    Handgraaf J W, Meijer E J, Gaigeot M P. Density-functional theory-based molecular simulation study of liquid methanol. J. Chem. Phys., 2004, 121 (20): 10111–10119. doi: 10.1063/1.1809595
    [27]
    McGrath M J, Kuo I F W, Siepmann J I. Liquid structures of water, methanol, and hydrogen fluoride at ambient conditions from first principles molecular dynamics simulations with a dispersion corrected density functional. Phys. Chem. Chem. Phys., 2011, 13 (44): 19943–19950. doi: 10.1039/c1cp21890e
    [28]
    Sieffert N, Bühl M, Gaigeot M P, et al. Liquid methanol from DFT and DFT/MM molecular dynamics simulations. J. Chem. Theory Comput., 2013, 9 (1): 106–118. doi: 10.1021/ct300784x
    [29]
    He J, Noto V D, Paddison S J. The structure of water–methanol mixtures under an electric field: Ab initio molecular dynamics simulations. Chem. Phys. Lett., 2015, 635: 99–106. doi: 10.1016/j.cplett.2015.06.049
    [30]
    Cassone G, Giaquinta P V, Saija F, et al. Liquid methanol under a static electric field. J. Chem. Phys., 2015, 142 (5): 054502. doi: 10.1063/1.4907010
    [31]
    Jindal A, Vasudevan S. Hydrogen bonding in the liquid state of linear alcohols: molecular dynamics and thermodynamics. J. Phys. Chem. B, 2020, 124 (17): 3548–3555. doi: 10.1021/acs.jpcb.0c01199
    [32]
    Jindal A, Vasudevan S. Geometry of OH···O interactions in the liquid state of linear alcohols from ab initio molecular dynamics simulations. Phys. Chem. Chem. Phys., 2020, 22 (12): 6690–6697. doi: 10.1039/D0CP00435A
    [33]
    Cassone G, Trusso S, Sponer J, et al. Electric field and temperature effects on the ab initio spectroscopy of liquid methanol. Appl. Sci., 2021, 11 (12): 5457. doi: 10.3390/app11125457
    [34]
    Grimme S. Semiempirical GGA-type density functional constructed with a long-range dispersion correction. J. Comput. Chem., 2006, 27 (15): 1787–1799. doi: 10.1002/jcc.20495
    [35]
    Becke A D. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A, 1988, 38: 3098. doi: 10.1103/PhysRevA.38.3098
    [36]
    Lee C, Yang W, Parr R G. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B, 1988, 37: 785–789. doi: 10.1103/PhysRevB.37.785
    [37]
    Maginn E J, Messerly R A, Carlson D J, et al. Best practices for computing transport properties 1. Self-diffusivity and viscosity from equilibrium molecular dynamics [article v1.0]. LiveCoMS, 2019, 1 (1): 6324. doi: 10.33011/livecoms.1.1.6324
    [38]
    Konrad M, Wenzel W. CONI-Net: Machine learning of separable intermolecular force fields. J. Chem. Theory Comput., 2021, 17 (8): 4996–5006. doi: 10.1021/acs.jctc.1c00328
    [39]
    Gastegger M, Behler J, Marquetand P. Machine learning molecular dynamics for the simulation of infrared spectra. Chem. Sci., 2017, 8 (10): 6924–6935. doi: 10.1039/C7SC02267K
    [40]
    Zhang Y L, Jiang B. Universal machine learning for the response of atomistic systems to external fields. Nat. Commun., 2023, 14 (1): 6424. doi: 10.1038/s41467-023-42148-y
    [41]
    Gastegger M, Schütt K T, Müller K R. Machine learning of solvent effects on molecular spectra and reactions. Chem. Sci., 2021, 12 (34): 11473–11483. doi: 10.1039/D1SC02742E
    [42]
    Manzhos S, Carrington Jr T. Neural network potential energy surfaces for small molecules and reactions. Chem. Rev., 2021, 121 (16): 10187–10217. doi: 10.1021/acs.chemrev.0c00665
    [43]
    Medders G R, Babin V, Paesani F. Development of a “first-principles” water potential with flexible monomers. III. Liquid phase properties. J. Chem. Theory Comput., 2014, 10 (8): 2906–2910. doi: 10.1021/ct5004115
    [44]
    Grisafi A, Wilkins D M, Csányi G, et al. Symmetry-adapted machine learning for tensorial properties of atomistic systems. Phys. Rev. Lett., 2018, 120 (3): 036002. doi: 10.1103/PhysRevLett.120.036002
    [45]
    Zhang L, Han J, Wang H, et al. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett., 2018, 120 (14): 143001. doi: 10.1103/PhysRevLett.120.143001
    [46]
    Nguyen T T, Székely E, Imbalzano G, et al. Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions. J. Chem. Phys., 2018, 148 (24): 241725. doi: 10.1063/1.5024577
    [47]
    Zhang L F, Wang H, Car R, et al. Phase diagram of a deep potential water model. Phys. Rev. Lett., 2021, 126 (23): 236001. doi: 10.1103/PhysRevLett.126.236001
    [48]
    Zhang Y L, Xia J F, Jiang B. Physically motivated recursively embedded atom neural networks: incorporating local completeness and nonlocality. Phys. Rev. Lett., 2021, 127 (15): 156002. doi: 10.1103/PhysRevLett.127.156002
    [49]
    Gartner T E, Piaggi P M, Car R, et al. Liquid-liquid transition in water from first principles. Phys. Rev. Lett., 2022, 129 (25): 255702. doi: 10.1103/PhysRevLett.129.255702
    [50]
    Bore S L, Paesani F. Realistic phase diagram of water from “first principles” data-driven quantum simulations. Nat. Commun., 2023, 14 (1): 3349. doi: 10.1038/s41467-023-38855-1
    [51]
    Yao K, Herr J E, Parkhill J. The many-body expansion combined with neural networks. J. Chem. Phys., 2017, 146 (1): 014106. doi: 10.1063/1.4973380
    [52]
    Li Y, Li H, Pickard F C, et al. Machine learning force field parameters from ab initio data. J. Chem. Theory Comput., 2017, 13 (9): 4492–4503. doi: 10.1021/acs.jctc.7b00521
    [53]
    Jindal S, Hsu P J, Phan H T, et al. Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning. Phys. Chem. Chem. Phys., 2022, 24 (44): 27263–27276. doi: 10.1039/D2CP04441B
    [54]
    Maldonado A M, Poltavsky I, Vassilev-Galindo V, et al. Modeling molecular ensembles with gradient-domain machine learning force fields. Digit. Discov., 2023, 2 (3): 871–880. doi: 10.1039/D3DD00011G
    [55]
    Chen B, Potoff J J, Siepmann J I. Monte Carlo calculations for alcohols and their mixtures with alkanes. Transferable potentials for phase equilibria. 5. United-atom description of primary, secondary, and tertiary alcohols. J. Phys. Chem. B, 2001, 105 (15): 3093–3104. doi: 10.1021/jp003882x
    [56]
    Ren P, Wu C, Ponder J W. Polarizable atomic multipole-based molecular mechanics for organic molecules. J. Chem. Theory Comput., 2011, 7 (10): 3143–3161. doi: 10.1021/ct200304d
    [57]
    Cheng B, Engel E A, Behler J, et al. Ab initio thermodynamics of liquid and solid water. Proc. Natl. Acad. Sci. U. S. A., 2019, 116 (4): 1110–1115. doi: 10.1073/pnas.1815117116
    [58]
    Ko H Y, Zhang L, Santra B, et al. Isotope effects in liquid water via deep potential molecular dynamics. Mol. Phys., 2019, 117 (22): 3269–3281. doi: 10.1080/00268976.2019.1652366
    [59]
    VandeVondele J, Krack M, Mohamed F, et al. Quickstep: Fast and accurate density functional calculations using a mixed Gaussian and plane waves approach. Comput. Phys. Commun., 2005, 167 (2): 103–128. doi: 10.1016/j.cpc.2004.12.014
    [60]
    Kühne T D, Iannuzzi M, Ben M D, et al. CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations. J. Chem. Phys., 2020, 152 (19): 194103. doi: 10.1063/5.0007045
    [61]
    Lippert G, Hutter J, Parrinello M. A hybrid Gaussian and plane wave density functional scheme. Mol. Phys., 1997, 92 (3): 477–487. doi: 10.1080/00268979709482119
    [62]
    Adamo C, Barone V. Toward reliable density functional methods without adjustable parameters: The PBE0 model. J. Chem. Phys., 1999, 110 (13): 6158–6170. doi: 10.1063/1.478522
    [63]
    Grimme S, Antony J, Ehrlich S, et al. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys., 2010, 132 (15): 154104. doi: 10.1063/1.3382344
    [64]
    Marsalek O, Markland T E. Quantum dynamics and spectroscopy of ab initio liquid water: the interplay of nuclear and electronic quantum effects. J. Phys. Chem. Lett., 2017, 8 (7): 1545–1551. doi: 10.1021/acs.jpclett.7b00391
    [65]
    Goedecker S, Teter M, Hutter J. Separable dual-space Gaussian pseudopotentials. Phys. Rev. B, 1996, 54 (3): 1703–1710. doi: 10.1103/PhysRevB.54.1703
    [66]
    Hartwigsen C, Goedecker S, Hutter J. Relativistic separable dual-space Gaussian pseudopotentials from H to Rn. Phys. Rev. B, 1998, 58 (7): 3641–3662. doi: 10.1103/PhysRevB.58.3641
    [67]
    Krack M. Pseudopotentials for H to Kr optimized for gradient-corrected exchange-correlation functionals. Theor. Chem. Acc., 2005, 114: 145–152. doi: 10.1007/s00214-005-0655-y
    [68]
    VandeVondele J, Hutter J. Gaussian basis sets for accurate calculations on molecular systems in gas and condensed phases. J. Chem. Phys., 2007, 127 (11): 114105. doi: 10.1063/1.2770708
    [69]
    Guidon M, Hutter J, VandeVondele J. Auxiliary density matrix methods for Hartree-Fock exchange calculations. J. Chem. Theory Comput., 2010, 6 (8): 2348–2364. doi: 10.1021/ct1002225
    [70]
    VandeVondele J, Hutter J. An efficient orbital transformation method for electronic structure calculations. J. Chem. Phys., 2003, 118 (10): 4365–4369. doi: 10.1063/1.1543154
    [71]
    Zhang Y L, Hu C, Jiang B. Embedded atom neural network potentials: efficient and accurate machine learning with a physically inspired representation. J. Phys. Chem. Lett., 2019, 10 (17): 4962–4967. doi: 10.1021/acs.jpclett.9b02037
    [72]
    Zhang Y L, Xia J F, Jiang B. REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems. J. Chem. Phys., 2022, 156 (11): 114801. doi: 10.1063/5.0080766
    [73]
    Zhang Y L, Lin Q D, Jiang B. Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization. WIREs Comput. Mol. Sci., 2023, 13 (3): e1645. doi: 10.1002/wcms.1645
    [74]
    Thompson A P, Aktulga H M, Berger R, et al. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput. Phys. Commun., 2022, 271: 108171 doi: 10.1016/j.cpc.2021.108171
    [75]
    Nosé S. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys., 1984, 81 (1): 511–519. doi: 10.1063/1.447334
    [76]
    Hoover W G. Canonical dynamics: Equilibrium phase-space distributions. Phys. Rev. A, 1985, 31 (3): 1695–1697. doi: 10.1103/PhysRevA.31.1695
    [77]
    Brehm M, Kirchner B. TRAVIS - a free analyzer and visualizer for Monte Carlo and molecular dynamics trajectories. J. Chem. Inf. Model., 2011, 51 (8): 2007–2023. doi: 10.1021/ci200217w
    [78]
    Brehm M, Thomas M, Gehrke S, et al. TRAVIS—A free analyzer for trajectories from molecular simulation. J. Chem. Phys., 2020, 152 (16): 164105. doi: 10.1063/5.0005078
    [79]
    Humphrey W, Dalke A, Schulten K. VMD: Visual molecular dynamics. J. Mol. Graph., 1996, 14 (1): 33–38. doi: 10.1016/0263-7855(96)00018-5
    [80]
    Ceriotti M, Fang W, Kusalik P G, et al. Nuclear quantum effects in water and aqueous systems: Experiment, theory, and current challenges. Chem. Rev., 2016, 116 (13): 7529–7550. doi: 10.1021/acs.chemrev.5b00674
    [81]
    Wang C C, Tan J Y, Liu L H. Ab initio molecular dynamics study of temperature and pressure-dependent infrared dielectric functions of liquid methanol. AIP Adv., 2017, 7 (3): 035115. doi: 10.1063/1.4978899
    [82]
    Hurle R L, Woolf L A. The effect of isotopic substitution on self-diffusion in methanol under pressure. Aust. J. Chem., 1980, 33 (9): 1947–1952. doi: 10.1071/CH9801947
    [83]
    Yadav V K, Chandra A. Dynamics of hydrogen bonds and vibrational spectral diffusion in liquid methanol from first principles simulations with dispersion corrected density functional. Chem. Phys., 2013, 415: 1–7. doi: 10.1016/j.chemphys.2013.01.029
    [84]
    Jukić I, Požar M, Lovrinčević B, et al. Universal features in the lifetime distribution of clusters in hydrogen-bonding liquids. Phys. Chem. Chem. Phys., 2021, 23 (35): 19537–19546. doi: 10.1039/D1CP02027G
    • Supplements(0)
    • Related Articles
    • Track Citations
  • 加载中

Catalog

    |
    Get Citation
    PDF
    XML

    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]
    van Leeuwen M E, Smit B. Molecular simulation of the vapor-liquid coexistence curve of methanol. J. Phys. Chem., 1995, 99 (7): 1831–1833. doi: 10.1021/j100007a006
    [15]
    Schnabel T, Srivastava A, Vrabec J, et al. Hydrogen bonding of methanol in supercritical CO2: comparison between 1H NMR spectroscopic data and molecular simulation results. J. Phys. Chem. B, 2007, 111 (33): 9871–9878. doi: 10.1021/jp0720338
    [16]
    Guevara-Carrion G, Nieto-Draghi C, Vrabec J, et al. Prediction of transport properties by molecular simulation: methanol and ethanol and their mixture. J. Phys. Chem. B, 2008, 112 (51): 16664–16674. doi: 10.1021/jp805584d
    [17]
    Jorgensen W L, Maxwell D S, Tirado-Rives J. Development and testing of the OPLS all-atom force field on conformational energetics and properties of organic liquids. J. Am. Chem. Soc., 1996, 118 (45): 11225–11236. doi: 10.1021/ja9621760
    [18]
    Wang J, Wolf R M, Caldwell J W, et al. Development and testing of a general amber force field. J. Comput. Chem., 2004, 25 (9): 1157–1174. doi: 10.1002/jcc.20035
    [19]
    Haughney M, Ferrario M, McDonald I R. Molecular-dynamics simulation of liquid methanol. J. Phys. Chem., 1987, 91 (19): 4934–4940. doi: 10.1021/j100303a011
    [20]
    Gonzalez-Salgado D, Vega C. A new intermolecular potential for simulations of methanol: The OPLS/2016 model. J. Chem. Phys., 2016, 145 (3): 034508. doi: 10.1063/1.4958320
    [21]
    Watanabe T, Ohashi K. Similarity and dissimilarity between water and methanol in solvent effects on the spectroscopic properties of aniline: Molecular dynamics and time-dependent DFT studies. Comput. Theor. Chem., 2022, 1215: 113850. doi: 10.1016/j.comptc.2022.113850
    [22]
    Galicia-Andrés E, Dominguez H, Pusztai L, et al. Composition dependence of thermodynamic, dynamic and dielectric properties of water–methanol model mixtures. Molecular dynamics simulation results with the OPLS-AA model for methanol. J. Mol. Liq., 2015, 212: 70–78. doi: 10.1016/j.molliq.2015.08.061
    [23]
    Tsuchida E, Kanada Y, Tsukada M. Density-functional study of liquid methanol. Chem. Phys. Lett., 1999, 311 (3/4): 236–240. doi: 10.1016/S0009-2614(99)00851-9
    [24]
    Pagliai M, Cardini G, Righini R, et al. Hydrogen bond dynamics in liquid methanol. J. Chem. Phys., 2003, 119 (13): 6655–6662. doi: 10.1063/1.1605093
    [25]
    Handgraaf J W, van Erp T S, Meijer E J. Ab initio molecular dynamics study of liquid methanol. Chem. Phys. Lett., 2003, 367 (5/6): 617–624. doi: 10.1016/S0009-2614(02)01779-7
    [26]
    Handgraaf J W, Meijer E J, Gaigeot M P. Density-functional theory-based molecular simulation study of liquid methanol. J. Chem. Phys., 2004, 121 (20): 10111–10119. doi: 10.1063/1.1809595
    [27]
    McGrath M J, Kuo I F W, Siepmann J I. Liquid structures of water, methanol, and hydrogen fluoride at ambient conditions from first principles molecular dynamics simulations with a dispersion corrected density functional. Phys. Chem. Chem. Phys., 2011, 13 (44): 19943–19950. doi: 10.1039/c1cp21890e
    [28]
    Sieffert N, Bühl M, Gaigeot M P, et al. Liquid methanol from DFT and DFT/MM molecular dynamics simulations. J. Chem. Theory Comput., 2013, 9 (1): 106–118. doi: 10.1021/ct300784x
    [29]
    He J, Noto V D, Paddison S J. The structure of water–methanol mixtures under an electric field: Ab initio molecular dynamics simulations. Chem. Phys. Lett., 2015, 635: 99–106. doi: 10.1016/j.cplett.2015.06.049
    [30]
    Cassone G, Giaquinta P V, Saija F, et al. Liquid methanol under a static electric field. J. Chem. Phys., 2015, 142 (5): 054502. doi: 10.1063/1.4907010
    [31]
    Jindal A, Vasudevan S. Hydrogen bonding in the liquid state of linear alcohols: molecular dynamics and thermodynamics. J. Phys. Chem. B, 2020, 124 (17): 3548–3555. doi: 10.1021/acs.jpcb.0c01199
    [32]
    Jindal A, Vasudevan S. Geometry of OH···O interactions in the liquid state of linear alcohols from ab initio molecular dynamics simulations. Phys. Chem. Chem. Phys., 2020, 22 (12): 6690–6697. doi: 10.1039/D0CP00435A
    [33]
    Cassone G, Trusso S, Sponer J, et al. Electric field and temperature effects on the ab initio spectroscopy of liquid methanol. Appl. Sci., 2021, 11 (12): 5457. doi: 10.3390/app11125457
    [34]
    Grimme S. Semiempirical GGA-type density functional constructed with a long-range dispersion correction. J. Comput. Chem., 2006, 27 (15): 1787–1799. doi: 10.1002/jcc.20495
    [35]
    Becke A D. Density-functional exchange-energy approximation with correct asymptotic behavior. Phys. Rev. A, 1988, 38: 3098. doi: 10.1103/PhysRevA.38.3098
    [36]
    Lee C, Yang W, Parr R G. Development of the Colle-Salvetti correlation-energy formula into a functional of the electron density. Phys. Rev. B, 1988, 37: 785–789. doi: 10.1103/PhysRevB.37.785
    [37]
    Maginn E J, Messerly R A, Carlson D J, et al. Best practices for computing transport properties 1. Self-diffusivity and viscosity from equilibrium molecular dynamics [article v1.0]. LiveCoMS, 2019, 1 (1): 6324. doi: 10.33011/livecoms.1.1.6324
    [38]
    Konrad M, Wenzel W. CONI-Net: Machine learning of separable intermolecular force fields. J. Chem. Theory Comput., 2021, 17 (8): 4996–5006. doi: 10.1021/acs.jctc.1c00328
    [39]
    Gastegger M, Behler J, Marquetand P. Machine learning molecular dynamics for the simulation of infrared spectra. Chem. Sci., 2017, 8 (10): 6924–6935. doi: 10.1039/C7SC02267K
    [40]
    Zhang Y L, Jiang B. Universal machine learning for the response of atomistic systems to external fields. Nat. Commun., 2023, 14 (1): 6424. doi: 10.1038/s41467-023-42148-y
    [41]
    Gastegger M, Schütt K T, Müller K R. Machine learning of solvent effects on molecular spectra and reactions. Chem. Sci., 2021, 12 (34): 11473–11483. doi: 10.1039/D1SC02742E
    [42]
    Manzhos S, Carrington Jr T. Neural network potential energy surfaces for small molecules and reactions. Chem. Rev., 2021, 121 (16): 10187–10217. doi: 10.1021/acs.chemrev.0c00665
    [43]
    Medders G R, Babin V, Paesani F. Development of a “first-principles” water potential with flexible monomers. III. Liquid phase properties. J. Chem. Theory Comput., 2014, 10 (8): 2906–2910. doi: 10.1021/ct5004115
    [44]
    Grisafi A, Wilkins D M, Csányi G, et al. Symmetry-adapted machine learning for tensorial properties of atomistic systems. Phys. Rev. Lett., 2018, 120 (3): 036002. doi: 10.1103/PhysRevLett.120.036002
    [45]
    Zhang L, Han J, Wang H, et al. Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett., 2018, 120 (14): 143001. doi: 10.1103/PhysRevLett.120.143001
    [46]
    Nguyen T T, Székely E, Imbalzano G, et al. Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions. J. Chem. Phys., 2018, 148 (24): 241725. doi: 10.1063/1.5024577
    [47]
    Zhang L F, Wang H, Car R, et al. Phase diagram of a deep potential water model. Phys. Rev. Lett., 2021, 126 (23): 236001. doi: 10.1103/PhysRevLett.126.236001
    [48]
    Zhang Y L, Xia J F, Jiang B. Physically motivated recursively embedded atom neural networks: incorporating local completeness and nonlocality. Phys. Rev. Lett., 2021, 127 (15): 156002. doi: 10.1103/PhysRevLett.127.156002
    [49]
    Gartner T E, Piaggi P M, Car R, et al. Liquid-liquid transition in water from first principles. Phys. Rev. Lett., 2022, 129 (25): 255702. doi: 10.1103/PhysRevLett.129.255702
    [50]
    Bore S L, Paesani F. Realistic phase diagram of water from “first principles” data-driven quantum simulations. Nat. Commun., 2023, 14 (1): 3349. doi: 10.1038/s41467-023-38855-1
    [51]
    Yao K, Herr J E, Parkhill J. The many-body expansion combined with neural networks. J. Chem. Phys., 2017, 146 (1): 014106. doi: 10.1063/1.4973380
    [52]
    Li Y, Li H, Pickard F C, et al. Machine learning force field parameters from ab initio data. J. Chem. Theory Comput., 2017, 13 (9): 4492–4503. doi: 10.1021/acs.jctc.7b00521
    [53]
    Jindal S, Hsu P J, Phan H T, et al. Capturing the potential energy landscape of large size molecular clusters from atomic interactions up to a 4-body system using deep learning. Phys. Chem. Chem. Phys., 2022, 24 (44): 27263–27276. doi: 10.1039/D2CP04441B
    [54]
    Maldonado A M, Poltavsky I, Vassilev-Galindo V, et al. Modeling molecular ensembles with gradient-domain machine learning force fields. Digit. Discov., 2023, 2 (3): 871–880. doi: 10.1039/D3DD00011G
    [55]
    Chen B, Potoff J J, Siepmann J I. Monte Carlo calculations for alcohols and their mixtures with alkanes. Transferable potentials for phase equilibria. 5. United-atom description of primary, secondary, and tertiary alcohols. J. Phys. Chem. B, 2001, 105 (15): 3093–3104. doi: 10.1021/jp003882x
    [56]
    Ren P, Wu C, Ponder J W. Polarizable atomic multipole-based molecular mechanics for organic molecules. J. Chem. Theory Comput., 2011, 7 (10): 3143–3161. doi: 10.1021/ct200304d
    [57]
    Cheng B, Engel E A, Behler J, et al. Ab initio thermodynamics of liquid and solid water. Proc. Natl. Acad. Sci. U. S. A., 2019, 116 (4): 1110–1115. doi: 10.1073/pnas.1815117116
    [58]
    Ko H Y, Zhang L, Santra B, et al. Isotope effects in liquid water via deep potential molecular dynamics. Mol. Phys., 2019, 117 (22): 3269–3281. doi: 10.1080/00268976.2019.1652366
    [59]
    VandeVondele J, Krack M, Mohamed F, et al. Quickstep: Fast and accurate density functional calculations using a mixed Gaussian and plane waves approach. Comput. Phys. Commun., 2005, 167 (2): 103–128. doi: 10.1016/j.cpc.2004.12.014
    [60]
    Kühne T D, Iannuzzi M, Ben M D, et al. CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations. J. Chem. Phys., 2020, 152 (19): 194103. doi: 10.1063/5.0007045
    [61]
    Lippert G, Hutter J, Parrinello M. A hybrid Gaussian and plane wave density functional scheme. Mol. Phys., 1997, 92 (3): 477–487. doi: 10.1080/00268979709482119
    [62]
    Adamo C, Barone V. Toward reliable density functional methods without adjustable parameters: The PBE0 model. J. Chem. Phys., 1999, 110 (13): 6158–6170. doi: 10.1063/1.478522
    [63]
    Grimme S, Antony J, Ehrlich S, et al. A consistent and accurate ab initio parametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu. J. Chem. Phys., 2010, 132 (15): 154104. doi: 10.1063/1.3382344
    [64]
    Marsalek O, Markland T E. Quantum dynamics and spectroscopy of ab initio liquid water: the interplay of nuclear and electronic quantum effects. J. Phys. Chem. Lett., 2017, 8 (7): 1545–1551. doi: 10.1021/acs.jpclett.7b00391
    [65]
    Goedecker S, Teter M, Hutter J. Separable dual-space Gaussian pseudopotentials. Phys. Rev. B, 1996, 54 (3): 1703–1710. doi: 10.1103/PhysRevB.54.1703
    [66]
    Hartwigsen C, Goedecker S, Hutter J. Relativistic separable dual-space Gaussian pseudopotentials from H to Rn. Phys. Rev. B, 1998, 58 (7): 3641–3662. doi: 10.1103/PhysRevB.58.3641
    [67]
    Krack M. Pseudopotentials for H to Kr optimized for gradient-corrected exchange-correlation functionals. Theor. Chem. Acc., 2005, 114: 145–152. doi: 10.1007/s00214-005-0655-y
    [68]
    VandeVondele J, Hutter J. Gaussian basis sets for accurate calculations on molecular systems in gas and condensed phases. J. Chem. Phys., 2007, 127 (11): 114105. doi: 10.1063/1.2770708
    [69]
    Guidon M, Hutter J, VandeVondele J. Auxiliary density matrix methods for Hartree-Fock exchange calculations. J. Chem. Theory Comput., 2010, 6 (8): 2348–2364. doi: 10.1021/ct1002225
    [70]
    VandeVondele J, Hutter J. An efficient orbital transformation method for electronic structure calculations. J. Chem. Phys., 2003, 118 (10): 4365–4369. doi: 10.1063/1.1543154
    [71]
    Zhang Y L, Hu C, Jiang B. Embedded atom neural network potentials: efficient and accurate machine learning with a physically inspired representation. J. Phys. Chem. Lett., 2019, 10 (17): 4962–4967. doi: 10.1021/acs.jpclett.9b02037
    [72]
    Zhang Y L, Xia J F, Jiang B. REANN: A PyTorch-based end-to-end multi-functional deep neural network package for molecular, reactive, and periodic systems. J. Chem. Phys., 2022, 156 (11): 114801. doi: 10.1063/5.0080766
    [73]
    Zhang Y L, Lin Q D, Jiang B. Atomistic neural network representations for chemical dynamics simulations of molecular, condensed phase, and interfacial systems: Efficiency, representability, and generalization. WIREs Comput. Mol. Sci., 2023, 13 (3): e1645. doi: 10.1002/wcms.1645
    [74]
    Thompson A P, Aktulga H M, Berger R, et al. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales. Comput. Phys. Commun., 2022, 271: 108171 doi: 10.1016/j.cpc.2021.108171
    [75]
    Nosé S. A unified formulation of the constant temperature molecular dynamics methods. J. Chem. Phys., 1984, 81 (1): 511–519. doi: 10.1063/1.447334
    [76]
    Hoover W G. Canonical dynamics: Equilibrium phase-space distributions. Phys. Rev. A, 1985, 31 (3): 1695–1697. doi: 10.1103/PhysRevA.31.1695
    [77]
    Brehm M, Kirchner B. TRAVIS - a free analyzer and visualizer for Monte Carlo and molecular dynamics trajectories. J. Chem. Inf. Model., 2011, 51 (8): 2007–2023. doi: 10.1021/ci200217w
    [78]
    Brehm M, Thomas M, Gehrke S, et al. TRAVIS—A free analyzer for trajectories from molecular simulation. J. Chem. Phys., 2020, 152 (16): 164105. doi: 10.1063/5.0005078
    [79]
    Humphrey W, Dalke A, Schulten K. VMD: Visual molecular dynamics. J. Mol. Graph., 1996, 14 (1): 33–38. doi: 10.1016/0263-7855(96)00018-5
    [80]
    Ceriotti M, Fang W, Kusalik P G, et al. Nuclear quantum effects in water and aqueous systems: Experiment, theory, and current challenges. Chem. Rev., 2016, 116 (13): 7529–7550. doi: 10.1021/acs.chemrev.5b00674
    [81]
    Wang C C, Tan J Y, Liu L H. Ab initio molecular dynamics study of temperature and pressure-dependent infrared dielectric functions of liquid methanol. AIP Adv., 2017, 7 (3): 035115. doi: 10.1063/1.4978899
    [82]
    Hurle R L, Woolf L A. The effect of isotopic substitution on self-diffusion in methanol under pressure. Aust. J. Chem., 1980, 33 (9): 1947–1952. doi: 10.1071/CH9801947
    [83]
    Yadav V K, Chandra A. Dynamics of hydrogen bonds and vibrational spectral diffusion in liquid methanol from first principles simulations with dispersion corrected density functional. Chem. Phys., 2013, 415: 1–7. doi: 10.1016/j.chemphys.2013.01.029
    [84]
    Jukić I, Požar M, Lovrinčević B, et al. Universal features in the lifetime distribution of clusters in hydrogen-bonding liquids. Phys. Chem. Chem. Phys., 2021, 23 (35): 19537–19546. doi: 10.1039/D1CP02027G
    Recommended articles
    TrendMD
    Volume 54 Issue 6 page: 0603
    Cover

    Article Metrics

    Article views (299) PDF downloads(665)
    Proportional views

    Copyright © Editorial Office of JUSTC, All Rights Reserved. 皖ICP备05002528号

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return