Tackling electrified interfaces using density functional theory and machine learning

Trulli
Modeling solvation effects and interfaces. Solid-liquid interfaces are approached from atomistic simulations, using either a force-field based description or machine learning approaches. Further coarse-graining of atomistic insights leads to fast and transferable continuum solvation schemes.

Recently, a number of studies have found that electrochemical processes in batteries and electrolyzers are highly sensitive to the electrified solid-liquid interface. In order to accurately predict reaction kinetics or the stability of electrochemical system compounds, it is essential to develop methods that can model such interfaces on a quantum accuracy level. In addition, for most state-of-the-art energy systems such as carbon materials, single atom catalysts, 2D materials (MoS2, etc.) or semiconductors the effect of electrification remains largely unknown and could potentially lead to the development of new technologies. In this project, we develop and apply several computational techniques from continuum over classical approaches up to full first-principles machine learning to upscale quantum chemical calculations with Density Functional Theory towards a full representation of the solid-liquid interface and its realistic reaction environment.

Apr 5, 2022

Related research projects/funds:
  • NRF (한국연구재단) Grant No. 2021R1C1C1008776 (신진연구)
  • Institute for Basic Science (IBS) for Molecular Spectroscopy and Dynamics

Subgroup members:
Stefan Ringe, Yevhen Horbatenko, Dianwei Hou, 이세연
Saeyeon Lee, Sahar Rabet, 김주희
Juhee Kim, Shuran Xu, 최보성
Bosung Choi

Related publications 13

  1. Intermaterial Hybridization as a Mechanism for Tunable Second-Harmonic Generation in Quantum Dot–Monolayer MoS2 Systems
    K. J. Lee et al., J. Phys. Chem. Lett. 2026, 17, 9877–9885.
    DOI
  2. Machine-learning enhanced simulations predict graphene is hydrophobic and microscopically not wetting transparent
    D. Hou et al., Nat. Commun. 2026, 17, 4792.
    DOI Cited by 3
  3. Atomic origins of electrochemical stability in acetate-based dual-cation water-in-salt electrolytes
    S. Palchowdhury et al., J. Chem. Phys. 2026, 164, 121102.
    DOI
  4. Conjugated Polyelectrolytes with Tunable Ionic Side Chains for Iodide-Mediated Pt Reduction in Photoelectrochemical H2 Generation
    J. M. Ha et al., Adv Energy Mater 2025, 0, e05450.
    DOI Cited by 2
  5. An implicit electrolyte model for plane wave density functional theory exhibiting nonlinear response and a nonlocal cavity definition
    S. M. R. Islam et al., J. Chem. Phys. 2023, 159, 234117.
    DOI Cited by 134
  6. Cation effects on electrocatalytic reduction processes at the example of the hydrogen evolution reaction
    S. Ringe, Curr Opin Electrochem 2023, 39, 101268.
    DOI Cited by 64
  7. Implicit Solvation Methods for Catalysis at Electrified Interfaces
    S. Ringe et al., Chem. Rev. 2022, 122, 10777 - 10820.
    DOI Cited by 297
  8. On the importance of the electric double layer structure in aqueous electrocatalysis
    S. Shin et al., Nat. Commun. 2022, 13, 174.
    DOI Cited by 308
  9. Understanding cation effects in electrochemical CO2 reduction
    S. Ringe et al., Energy Environ. Sci. 2019, 12, 3001 - 3014.
    DOI Cited by 825 HIGHLIGHT HOT COVER
  10. A Two-Dimensional MoS2 Catalysis Transistor by Solid-State Ion Gating Manipulation and Adjustment (SIGMA)
    Y. Wu et al., Nano Lett. 2019, 19, 7293 - 7300.
    DOI Cited by 56
  11. Generalized molecular solvation in non-aqueous solutions by a single parameter implicit solvation scheme
    C. Hille et al., J. Chem. Phys. 2019, 150, 041710.
    DOI Cited by 62
  12. Transferable ionic parameters for first-principles Poisson-Boltzmann solvation calculations: Neutral solutes in aqueous monovalent salt solutions
    S. Ringe et al., J. Chem. Phys. 2017, 146, 134103.
    DOI Cited by 51 COVER
  13. Function-Space-Based Solution Scheme for the Size-Modified Poisson-Boltzmann Equation in Full-Potential DFT
    S. Ringe et al., J. Chem. Theory Comput. 2016, 12, 4052 - 4066.
    DOI Cited by 88