전산나노소재 연구실 Computational Nano Materials Design Lab

 As the interest in machine learning increases, the interest in databases for use in machine learning is also increasing. Despite the increasing demand for databases in the material field, there is no clear database to date. Therefore, in this project, we intend to build a database based on research results in the field of materials existing in Internet documents through natural language processing techniques.  HfO2-based FeRAM has been spotlighted as a key material for future semiconductor technologies such as logic-in-memory, neuromorphic computing, and negative capacitance FET, but it is facing difficulties in practical use due to its high coercive field and low domain wall velocity. Therefore, this study aims to understand and investigate the phenomenon related to the movement of domain walls under the electric field of HfO2-based ferroelectric materials at the atomic level through machine learning and molecular dynamics simulation.  Protonic ceramic electrolysis cell is a next-generation hydrogen production system that can be operated at low temperatures and generates pure hydrogen from the fuel electrode. High chemical stability and proton conductivity material design is required as air electrode and electrolyte materials for PCEC. Using first-principle calculations, the mechanisms of proton conduction and water splitting reactions are identified, and doping materials are explored. Based on the first principle calculation data, PCEC materials are optimized in a wide composition space through a material performance prediction machine learning model.  Inorganic cathode materials that are currently commercialized have environmental problems such as large amount of carbon emissions generated during the process and the use of expensive metals such as Co and Ni. Due to