Department of Physics, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan 44610, Republic of Korea.
School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea.
ACS Appl Mater Interfaces. 2023 Jun 14;15(23):27995-28007. doi: 10.1021/acsami.3c03323. Epub 2023 May 26.
While economical and effective catalysts are required for sustainable hydrogen production, low-dimensional interfacial engineering techniques have been developed to improve the catalytic activity in the hydrogen evolution reaction (HER). In this study, we used density functional theory (DFT) calculations to measure the Gibbs free energy change (Δ) in hydrogen adsorption in two-dimensional lateral heterostructures (LHSs) MX/M'X' (MoS/WS, MoS/WSe, MoSe/WS, MoSe/WSe, MoTe/WSe, MoTe/WTe, and WS/WSe) and MX/M'X' (NbS/ZnO, NbSe/ZnO, NbS/GaN, MoS/ZnO, MoSe/ZnO, MoS/AlN, MoS/GaN, and MoSe/GaN) at several different positions near the interface. Compared to the interfaces of LHS MX/M'X' and the surfaces of the monolayer MX and MX, the interfaces of LHS MX/M'X' display greater hydrogen evolution reactivity due to their metallic behavior. The hydrogen absorption is stronger at the interfaces of LHS MX/M'X', and that facilitates proton accessibility and increases the usage of catalytically active sites. Here, we develop three types of descriptors that can be used universally in 2D materials and can explain changes in Δ for different adsorption sites in a single LHS using only the basic information of the LHSs (type and number of neighboring atoms to the adsorption points). Using the DFT results of the LHSs and the various experimental data of atomic information, we trained machine learning (ML) models with the chosen descriptors to predict promising combinations and adsorption sites for HER catalysts among the LHSs. Our ML model achieved an score of 0.951 (regression) and an score of 0.749 (classification). Furthermore, the developed surrogate model was implemented to predict the structures in the test set and was based on confirmation from the DFT calculations via Δ values. The LHS MoS/ZnO is the best candidate for HER among 49 candidates considered using both DFT and ML models because it has a Δ of -0.02 eV on top of O at the interface position and requires only -171 mV of overpotential to obtain the standard current density (10 A/cm).
虽然需要经济高效的催化剂来实现可持续的氢气生产,但已经开发出低维界面工程技术来提高析氢反应(HER)中的催化活性。在这项研究中,我们使用密度泛函理论(DFT)计算测量了二维横向异质结构(LHS)MX/M'X'(MoS/WS、MoS/WSe、MoSe/WS、MoSe/WSe、MoTe/WSe、MoTe/WTe 和 WS/WSe)和 MX/M'X'(NbS/ZnO、NbSe/ZnO、NbS/GaN、MoS/ZnO、MoSe/ZnO、MoS/AlN、MoS/GaN 和 MoSe/GaN)在界面附近几个不同位置的氢吸附吉布斯自由能变化(Δ)。与 LHS MX/M'X'的界面和单层 MX 和 MX 的表面相比,由于 LHS MX/M'X'的金属行为,其具有更大的析氢反应活性。LHS MX/M'X'界面处的氢吸收更强,这有利于质子可及性并增加催化活性位点的利用率。在这里,我们开发了三种类型的描述符,这些描述符可以在二维材料中普遍使用,并且仅使用 LHS 的基本信息(吸附点的相邻原子的类型和数量)就可以解释单个 LHS 中不同吸附位点的Δ变化。使用 LHS 的 DFT 结果和各种原子信息的实验数据,我们使用所选描述符训练了机器学习(ML)模型,以预测 LHS 中 HER 催化剂的有希望的组合和吸附位点。我们的 ML 模型的 得分达到 0.951(回归)和 0.749(分类)。此外,所开发的替代模型用于预测测试集中的结构,并通过 Δ 值基于 DFT 计算进行验证。在考虑的 49 个候选物中,基于 DFT 和 ML 模型,LHS MoS/ZnO 是 HER 的最佳候选物,因为它在界面位置的 O 上方的 Δ 值为-0.02 eV,并且仅需要-171 mV 的过电势即可获得标准电流密度(10 A/cm)。