나노입자의 전자구조 및 Poubaix diagram 예측 머신러닝 기술
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한상수 KIST For acceleration of material design, understanding a correlation between material structures and their properties is critical. Of various material properties, an electronic density of states (DOS) is one of key factors in condensed matter physics and material science that determines the properties of materials. First-principles density-functional theory (DFT) calculations have typically been used to obtain the DOS despite the considerable computation cost. Recently, we have developed a extremely fast machine-learning method for predicting the DOS patterns of not only bulk or surface structures but also nanoparticles (NPs) in multi-component alloy systems by a combination of principle component analysis and crystal graph convolutional neural network. In the first part of this talk, I will cover the machine-learning model for the DOS prediction. And then, I will move to a topic regarding the stability issue of metallic NPs. For electrochemical applications of the NPs (e.g., ORR, NRR, CO2RR catalysts), their stabilities must be considered. For doing this, we have recently developed a machine-learning model for predicting Poubaix diagrams of metallic NPs, which will be presented in this talk. |