AIMat/
Articles/
2511002381
  • Open Access
  • Article

Supervised Machine Learning Assisted Development of Hybrid Solvation Model for Simulating Graphene-Water Interface

  • Jordan Clive Barker,   
  • William Wen,   
  • Yun Wang *

Received: 30 Sep 2025 | Revised: 24 Nov 2025 | Accepted: 24 Nov 2025 | Published: 26 Nov 2025

Abstract

The electrified graphene-water interface is a vital component in many energy storage applications. However, understanding the interfacial properties is challenging due to the requirement of a high-quality atomic interfacial model.  Recently, the hybrid solvation model, including the computationally affordable implicit solvation model and a thin layer of explicit water solvent slab next to the solid, has become a promising approach to address this issue. The identification of the rational explicit water slab thickness holds the key to the computational results by using this hybrid solvation model. In this study, we present a framework combining ab initio molecular dynamics (AIMD) and supervised machine learning (ML) to address this challenge. Based on the database from the AIMD simulations, the relationship between the total energy of the system and the distance from the oxygen in water molecules to the graphene was successfully identified through supervised ML. Our results further demonstrate that the first few layers of water next to the graphene play the decisive role in the change of the total energy. The cutoff thickness of 7 Å can reproduce the majority of the impact of the solvent on the total energy change of the water-graphene system. The success of this ML-assisted platform suggests it can also be used as a protocol to build the hybrid solvation model for understanding other electrified solid-liquid interfaces.

References 

  • 1.
    Ray, S.C. Applications of Graphene and Graphene-Oxide Based Nanomaterials; William Andrew Publishing: Norwich, NY, USA, 2015. https://doi.org/10.1016/C2014-0-02615-9.
  • 2.
    Wang, X.; Zhi, L.; Müllen, K. Transparent, Conductive Graphene Electrodes for Dye-Sensitized Solar Cells. Nano Lett. 2008, 8, 323–327. https://doi.org/10.1021/nl072838r.
  • 3.
    Balandin, A.A.; Ghosh, S.; Bao, W.; et al. Superior Thermal Conductivity of Single-Layer Graphene. Nano Lett. 2008, 8, 902–907. https://doi.org/10.1021/nl0731872.
  • 4.
    Kim, T.Y.; Park, C.-H.; Marzari, N. The Electronic Thermal Conductivity of Graphene. Nano Lett. 2016, 16, 2439–2443. https://doi.org/10.1021/acs.nanolett.5b05288.
  • 5.
    Yavari, F.; Koratkar, N. Graphene-Based Chemical Sensors. J. Phys. Chem. Lett. 2012, 3, 1746–1753. https://doi.org/10.1021/jz300358t.
  • 6.
    Hill, E.W.; Vijayaragahvan, A.; Novoselov, K. Graphene Sensors. IEEE Sens. J. 2011, 11, 3161–3170. https://doi.org/10.1109/JSEN.2011.2167608.
  • 7.
    Zhou, Y.; Bao, Q.; Tang, L.A.L.; et al. Hydrothermal Dehydration for the “Green” Reduction of Exfoliated Graphene Oxide to Graphene and Demonstration of Tunable Optical Limiting Properties. Chem. Mater. 2009, 21, 2950–2956. https://doi.org/10.1021/cm9006603.
  • 8.
    Huang, C.; Li, C.; Shi, G. Graphene based catalysts. Energy Environ. Sci. 2012, 5, 8848–8868. https://doi.org/10.1039/C2EE22238H
  • 9.
    Julkapli, N.M.; Bagheri, S. Graphene supported heterogeneous catalysts: An overview. Int. J. Hydrogen Energy 2015, 40, 948–979. https://doi.org/10.1016/j.ijhydene.2014.10.129.
  • 10.
    Falkovsky, L.A. Optical properties of graphene. J. Phys. Conf. Ser. 2008, 129, 012004. https://doi.org/10.1088/1742-6596/129/1/012004.
  • 11.
    Shao, Y.; El-Kady, M.F.; Wang, L.J.; et al. Graphene-based materials for flexible supercapacitors. Chem. Soc. Rev. 2015, 44, 3639–3665. https://doi.org/10.1039/C4CS00316K.
  • 12.
    Nag, A.; Mitra, A.; Mukhopadhyay, S.C. Graphene and its sensor-based applications: A review. Sens. Actuators A Phys. 2018, 270, 177–194. https://doi.org/10.1016/j.sna.2017.12.028.
  • 13.
    Ghosh, S.; Barg, S.; Jeong, S.M.; et al. Heteroatom-Doped and Oxygen-Functionalized Nanocarbons for High-Performance Supercapacitors. Adv. Energy Mater. 2020, 10, 2001239. https://doi.org/10.1002/aenm.202001239.
  • 14.
    Park, S.; Ruoff, R.S. Chemical methods for the production of graphenes. Nat. Nanotechnol. 2009, 4, 217–224. https://doi.org/10.1038/nnano.2009.58.
  • 15.
    Ke, Q.; Wang, J. Graphene-based materials for supercapacitor electrodes—A review. J. Mater. 2016, 2, 37–54. https://doi.org/10.1016/j.jmat.2016.01.001.
  • 16.
    Huang, S.; Zhu, X.; Sarkar, S.; et al. Challenges and opportunities for supercapacitors. APL Mater. 2019, 7, 100901. https://doi.org/10.1063/1.5116146.
  • 17.
    Akaishi, A.; Yonemaru, T.; Nakamura, J. Formation of Water Layers on Graphene Surfaces. ACS Omega 2017, 2, 2184–2190. https://doi.org/10.1021/acsomega.7b00365.
  • 18.
    Wang, Y.; Tang, F.; Yu, X.; et al. Heterodyne-Detected Sum-Frequency Generation Vibrational Spectroscopy Reveals Aqueous Molecular Structure at the Suspended Graphene/Water Interface. Angew. Chem. Int. Ed. 2024, 63, e202319503. https://doi.org/10.1002/anie.202319503.
  • 19.
    Bouzid, A.; Gono, P.; Pasquarello, A. Reaction pathway of oxygen evolution on Pt(111) revealed through constant Fermi level molecular dynamics. J. Catal. 2019, 375, 135–139.
  • 20.
    Noordhoek, K.; Bartel, C.J. Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials. Nanoscale 2024, 16, 6365–6382. https://doi.org/10.1039/D3NR06468A.
  • 21.
    Islam, S.M.R.; Khezeli, F.; Ringe, S.; et al. An implicit electrolyte model for plane wave density functional theory exhibiting nonlinear response and a nonlocal cavity definition. J. Chem. Phys. 2023, 159, 234117. https://doi.org/10.1063/5.0176308.
  • 22.
    Mathew, K.; Kolluru, V.S.C.; Mula, S.; et al. Implicit self-consistent electrolyte model in plane-wave density-functional theory. J. Chem. Phys. 2019, 151, 234101. https://doi.org/10.1063/1.5132354.
  • 23.
    Ringe, S.; Hörmann, N.G.; Oberhofer, H.; et al. Implicit Solvation Methods for Catalysis at Electrified Interfaces. Chem. Rev. 2022, 122, 10777–10820. https://doi.org/10.1021/acs.chemrev.1c00675.
  • 24.
    Hinsch, J.J.; Bouzid, A.; Barker, J.C.; et al. Revisiting the Electrified Pt(111)/Water Interfaces through an Affordable Double-Reference Ab Initio Approach. J. Phys. Chem. C 2023, 127, 19857–19866. https://doi.org/10.1021/acs.jpcc.3c05425.
  • 25.
    Xu, P.; von Rueden, A.D.; Schimmenti, R.; et al. Optical method for quantifying the potential of zero charge at the platinum–water electrochemical interface. Nat. Mater. 2023, 22, 503–510. https://doi.org/10.1038/s41563-023-01474-8.
  • 26.
    Hinsch, J.J.; White, J.J.; Wang, Y. Theoretical investigation on potential of zero free charge of (111) and (100) surfaces of Group 10 and 11 metals. Comput. Theor. Chem. 2024, 1232, 114462. https://doi.org/10.1016/j.comptc.2024.114462.
  • 27.
    Kresse, G.; Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 1996, 6, 15–50. https://doi.org/10.1016/0927-0256(96)00008-0.
  • 28.
    Kresse, G.; Furthmüller, J. Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set. Phys. Rev. B 1996, 54, 11169–11186. https://doi.org/10.1103/PhysRevB.54.11169.
  • 29.
    Wang, V.; Xu, N.; Liu, J.-C.; et al. VASPKIT: A user-friendly interface facilitating high-throughput computing and analysis using VASP code. Comput. Phys. Commun. 2021, 267, 108033. https://doi.org/10.1016/j.cpc.2021.108033.
  • 30.
    Kresse, G.; Joubert, D. From ultrasoft pseudopotentials to the projector augmented-wave method. Phys. Rev. B 1999, 59, 1758–1775. https://doi.org/10.1103/PhysRevB.59.1758.
  • 31.
    Klimeš, J.; Bowler, D.R.; Michaelides, A. Chemical accuracy for the van der Waals density functional. J. Phys. Condens. Matter 2010, 22, 022201. https://doi.org/10.1088/0953-8984/22/2/022201.
  • 32.
    Yang, W.; Ni, M.; Ren, X.; et al. Graphene in Supercapacitor Applications. Curr. Opin. Colloid Interface Sci. 2015, 20, 416–428. https://doi.org/10.1016/j.cocis.2015.10.009.
  • 33.
    Tan, Y.B.; Lee, J.-M. Graphene for supercapacitor applications. J. Mater. Chem. A 2013, 1, 14814–14843. https://doi.org/10.1039/C3TA12193C.
  • 34.
    Papageorgiou, D.G.; Kinloch, I.A.; Young, R.J. Mechanical properties of graphene and graphene-based nanocomposites. Prog. Mater. Sci. 2017, 90, 75–127. https://doi.org/10.1016/j.pmatsci.2017.07.004.
  • 35.
    X-Ability Co., Ltd. Winmostar V11.13.2; X-Ability Co., Ltd.: Tokyo, Japan, 2025.
  • 36.
    Mortier, F.; Zhao, D.; Otani, M.; et al. First-principles investigation of electrified monolayered MoS2/water interface. Surf. Sci. 2026, 764, 122870. https://doi.org/10.1016/j.susc.2025.122870.
  • 37.
    VandeVondele, J.; Mohamed, F.; Krack, M.; et al. The influence of temperature and density functional models in ab initio molecular dynamics simulation of liquid water. J. Chem. Phys. 2004, 122, 014515. https://doi.org/10.1063/1.1828433.
  • 38.
    Sit, P.H.L.; Marzari, N. Static and dynamical properties of heavy water at ambient conditions from first-principles molecular dynamics. J. Chem. Phys. 2005, 122, 204510. https://doi.org/10.1063/1.1908913.
  • 39.
    Soper, A.K. Water and Ice Structure in the Range 220—365 K from Radiation Total Scattering Experiments; IOS Press: Amsterdam, The Netherlands, 2014. https://doi.org/10.3254/978-1-61499-507-4-151.
  • 40.
    Paszke, A.; Gross, S.; Chintala, S.; et al. Automatic Differentiation in PyTorch; NIPS-W: San Diego, CA, USA, 2017.
  • 41.
    Kingma, D.P.; Ba, J. Adam: A Method for Stochastic Optimization. arXiv 2014, arXiv:1412.6980.
Share this article:
Download PDF
Issue
Volume 1, Issue 1
How to Cite
Barker, J. C.; Wen, W.; Wang, Y. Supervised Machine Learning Assisted Development of Hybrid Solvation Model for Simulating Graphene-Water Interface. AI for Materials 2025, 1 (1), 3.
RIS
BibTex
Copyright & License
article copyright Image
Copyright (c) 2025 by the authors.