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도로포장 재료 개발을 위한 소재정보학과 분자동역학 활용연구 방법 고찰(Ⅱ) : 인공지능 기술을 적용한 재료·소재 연구 동향 KCI 등재
Research Methods Utilizing Materials Informatics and Molecular Dynamics for the Development of Road-Pavement Materials (II): Material Research Trends Using Artificial Intelligence Technology
pp.1-11
This paper explores a convergent approach that combines advanced informatics and computational science to develop road-paving materials. It also analyzes research trends that apply artificial-intelligence technologies to propose research directions for developing new materials and optimizing them for road pavements. This paper reviews various research trends in material design and development, including studies on materials and substances, quantitative structure–activity/property relationship (QSAR/QSPR) research, molecular data, and descriptors, and their applications in the fields of biomedicine, composite materials, and road-construction materials. Data representation is crucial for applying deep learning to construction-material data. Moreover, selecting significant variables for training is important, and the importance of these variables can be evaluated using Pearson’s correlation coefficients or ensemble techniques. In selecting training data and applying appropriate prediction models, the author intends to conduct future research on property prediction and apply string-based representations and generative adversarial networks (GANs). The convergence of artificial intelligence and computational science has enabled transformative changes in the field of material development, contributing significantly to enhancing the performance of road-paving materials. The future impacts of discovering new materials and optimizing research outcomes are highly anticipated.
1. 연구배경 및 목적
1.1. 연구배경
1.2. 연구목적 및 방법
2. 소재 설계를 위한 QSAR/QSPR 및 기계학습
2.1. 재료·소재 연구와 QSAR/QSPR 연구
2.2. QSAR/QSPR과 다양한 ML기법
3. 각 분야에서 ML 활용 연구
3.1. 바이오 분야에서의 인공지능 활용 연구
3.2. 복합소재와 인공지능 활용 연구
3.3. 도로 건설 소재 분야에서의 ML 연구 고찰
4. 결론
REFERENCES
