This study proposes a methodology for predicting the physical properties such as the density of polymer composites, including asphalt binders, and evaluates its feasibility by identifying the quantitative relationship between the structure and properties of individual polymers. To this end, features are constructed using molecular dynamics (MD) simulation results and descriptor calculation tools. This study investigates the changes in the calculated density depending on the characteristics of the training dataset and analyzes the feature characteristics across datasets to identify key features. In this study, 2,415 hydrocarbon and binder-derived polymer molecules were analyzed using MD simulations and 2,790 chemical descriptors generated using alvaDesc. The features were pre-processed using correlation filtering, PCA, and recursive feature elimination. The XGBoost models were trained using k-fold cross-validation and Optuna optimization. SHAP analysis was used to interpret feature contributions. The variables influencing the density prediction differed between the hydrocarbon and binder groups. However, the hydrogen atom count (H), van der Waals energy, and descriptors such as SpMAD_EA_LboR consistently had a strong impact. The trained models achieved high accuracy (R² > 0.99) across different datasets, and the SHAP results revealed that the edge adjacency, topological, and 3D geometrical descriptors were critical. In terms of predictive accuracy and interpretability, the integrated MDQSPR framework demonstrated high reliability for estimating the properties of individual binder polymers. This approach contributed to a molecular-level understanding and facilitated the development of ecofriendly and efficient modifiers for asphalt binders.

ABSTRACT
1. 연구배경 및 목적
    1.1. 연구배경
    1.2. 연구 목적
2. 연구 방법
    2.1. 정량적 구조-물성 상관관계
    2.2. 개별 고분자에 대한 설명자 결정
    2.3. 자료 전처리 및 주요변수 선정
    2.4. 학습 모형 및 학습 방법
    2.5. 결과 분석 방법
3. 결과 분석
    3.1. 탄화수소기반 분자구조에 대한 학습
    3.2. 개별 아스팔트 바인더 분자구조에 대한 학습
    3.3. 탄화수소와 아스팔트 바인더 분자구조에 대한 학습
4. 결론 및 요약
    4.1. 결론
REFERENCES

• 윤태영(한국건설기술연구원 연구위원) | Yun Taeyoung Corresponding author