Enhanced Hydrogen Evolution Performance at the Lateral Interface between Two Layered Materials Predicted with Machine Learning
- Authors
- Pham, Thi Hue; Kim, Eunsong; Min, Kyoungmin; Shin, Young-Han
- Issue Date
- May-2023
- Publisher
- AMER CHEMICAL SOC
- Keywords
- lateral heterostructures; TMDCs; interfaces; DFT; machine learning; hydrogen evolution reactions; exchange current density; overpotential
- Citation
- ACS APPLIED MATERIALS & INTERFACES, v.15, no.23, pp.27995 - 28007
- Journal Title
- ACS APPLIED MATERIALS & INTERFACES
- Volume
- 15
- Number
- 23
- Start Page
- 27995
- End Page
- 28007
- URI
- http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/44044
- DOI
- 10.1021/acsami.3c03323
- ISSN
- 1944-8244
- Abstract
- While economical and effective catalysts are requiredfor sustainablehydrogen production, low-dimensional interfacial engineering techniqueshave been developed to improve the catalytic activity in the hydrogenevolution reaction (HER). In this study, we used density functionaltheory (DFT) calculations to measure the Gibbs free energy change(Delta G (H)) in hydrogen adsorption intwo-dimensional lateral heterostructures (LHSs) MX2/M'X'(2) (MoS2/WS2, MoS2/WSe2, MoSe2/WS2, MoSe2/WSe2, MoTe2/WSe2, MoTe2/WTe2, and WS2/WSe2) and MX2/M'X'(NbS2/ZnO, NbSe2/ZnO, NbS2/GaN, MoS2/ZnO, MoSe2/ZnO, MoS2/AlN, MoS2/GaN, and MoSe2/GaN) at several different positions nearthe interface. Compared to the interfaces of LHS MX2/M'X'(2) and the surfaces of the monolayer MX2 and MX,the interfaces of LHS MX2/M'X' display greaterhydrogen evolution reactivity due to their metallic behavior. Thehydrogen absorption is stronger at the interfaces of LHS MX2/M'X', and that facilitates proton accessibility andincreases the usage of catalytically active sites. Here, we developthree types of descriptors that can be used universally in 2D materialsand can explain changes in Delta G (H) fordifferent adsorption sites in a single LHS using only the basic informationof the LHSs (type and number of neighboring atoms to the adsorptionpoints). Using the DFT results of the LHSs and the various experimentaldata of atomic information, we trained machine learning (ML) modelswith the chosen descriptors to predict promising combinations andadsorption sites for HER catalysts among the LHSs. Our ML model achievedan R (2) score of 0.951 (regression) andan F (1) score of 0.749 (classification).Furthermore, the developed surrogate model was implemented to predictthe structures in the test set and was based on confirmation fromthe DFT calculations via Delta G (H) values.The LHS MoS2/ZnO is the best candidate for HER among 49candidates considered using both DFT and ML models because it hasa Delta G (H) of -0.02 eV on topof O at the interface position and requires only -171 mV ofoverpotential to obtain the standard current density (10 A/cm(2)).
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