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.

PURPOSES : As evaluation methods for road paving materials become increasingly complex, there is a need for a method that combines computational science and informatics for new material development. This study aimed to develop a rational methodology for applying molecular dynamics and AI-based material development techniques to the development of additives for asphalt mixtures. METHODS : This study reviewed relevant literature to analyze various molecular models, evaluation methods, and metrics for asphalt binders. It examined the molecular structures and conditions required for calculations using molecular dynamics and evaluated methods for assessing the interactions between additives and asphalt binders, as well as properties such as the density, viscosity, and glass transition temperature. Key evaluation indicators included the concept and application of interaction energy, work of adhesion, cohesive energy density, solubility parameters, radial distribution function, energy barriers, elastic modulus, viscosity, and stress-strain curves. RESULTS : The study identified key factors and conditions for effectively evaluating the physical properties of asphalt binders and additives. It proposed selective application methods and ranges for the layer structure, temperature conditions, and evaluation metrics, considering the actual conditions in which asphalt binders were used. Additional elements and conditions considered in the literature may be further explored, considering the computational demands. CONCLUSIONS : This study devised a methodology for evaluating the physical properties of asphalt binders considering temperature and aging. It reviewed and selected useful indicators for assessing the interaction between asphalt binders, additives, and modified asphalt binders and aggregates under various environmental conditions. By applying the proposed methods and linking the results with informatics, the interaction between asphalt binders and additives could be efficiently evaluated, serving as a reliable method for new material development.