데이터 최적화 기법을 도입한 기계적 물성치 예측 모델과 새로운 2차원 소재의 발견

다국어 초록 (Multilingual Abstract)

Two-dimensional (2D) materials are attractive materials. Many studies are being conducted because of their unique characteristics. However, there is lack of information about properties of 2D materials. Therefore, this study attempted to solve this problem by developing a machine learning (ML) model that predicts mechanical properties of 2D materials. In addition, a 2D materials generation framework was developed using a classification model and a deep learning-based generative model. ML model to predict mechanical properties is trained from existing 2D database and reduces the uncertainty of prediction through data optimization techniques. Potential 2D materials are discovered through screening processes such as measuring structure and atomic similarities. We believe that the developing of ML model and framework for finding new 2D materials could open a new chapter in material science

목차 (Table of Contents)

• Abstract
• 1. 서론
• 2. 이론 및 실험
• 3. 결론
• 참고문헌