Tackling electrified interfaces using density functional theory and machine learning
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.
Related research projects/funds:
- NRF (한국연구재단) Grant No. 2021R1C1C1008776 (신진연구)
- Institute for Basic Science (IBS) for Molecular Spectroscopy and Dynamics
Stefan Ringe, Hafiz Ghulam Abbas, Yevhen Horbatenko, Dianwei Hou, 한승창
Seungchang Han, 김서영
Seoyeong Kim, 이세연
Saeyeon Lee, 김동진
Dongjin Kim, 설호성
- S. Ringe†* et al., Understanding cation effects in electrochemical CO2 reduction, Energy Environ. Sci. 2019, 12, 3001 - 3014.
- S. Shin† et al., On the importance of the electric double layer structure in aqueous electrocatalysis, Nat. Commun. 2022, 13, 174.
- Y. Wu† et al., A Two-Dimensional MoS2 Catalysis Transistor by Solid-State Ion Gating Manipulation and Adjustment (SIGMA), Nano Lett. 2019, 19, 7293 - 7300.