http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
기계학습을 활용한 2차원 이종접합 헤테로구조의 수소발생 성능 예측
김은송(Eunsong Kim),팜 티 후에(Thi Hue Pham),민경민(Kyoungmin Min),신영한(Young-Han Shin) 대한기계학회 2023 대한기계학회 춘추학술대회 Vol.2023 No.11
While efficient and cost-effective catalysts are needed for long-term hydrogen production, low-dimensional interfacial approaches have been developed to increase catalytic activity performance in hydrogen evolution reaction (HER). We calculated the Gibbs free energy change (ΔGH) in hydrogen adsorption in the two-dimensional lateral heterostructures (LHS) at various adsorption points in each unit-cell using density functional theory (DFT). We developed three types of descriptors (position feature, weight feature, average feature) that may be utilized universally in 2D materials, also can explain ΔGH based on different adsorption sites in a single LHS combining basic LHS information (the type and quantity of neighboring atoms around the adsorption point). Furthermore, we trained machine learning (ML) models with the specified descriptors to predict the potential conjunction and adsorption sites within the LHS for HER catalysts. ML model in this research reached R2 score of 0.95. Additionally, 8 LHS materials were examined successfully with the ML model.