Epoxy resin is an of the most widely used adhesives for various applications owing to its outstanding properties. The performance of epoxy systems varies significantly depending on the composition of the base resin and curing agent. However, there are limitations in exploring numerous formulations of epoxy resins to optimize adhesive properties because of the expense and time-consuming nature of the trial-and-error process. Herein, molecular dynamics (MD) simulations and machine learning (ML) methods were used to overcome these challenges and predict the adhesive properties of epoxy resin. Datasets for diverse epoxy adhesive formulations were constructed by considering the degree of crosslinking, density, free volume, cohesive energy density, modulus, and glass transition temperature. A linear correlation analysis demonstrated that the content of the curing agents, especially dicyandiamide (DICY), had the greatest correlation with the cohesive energy density. Moreover, the content of tetraglycidyl methylene dianiline (TGMDA) had the highest correlation with the modulus, and the content of diglycidyl ether of bisphenol A (DGEBA) had the highest correlation with the glass transition temperature. An optimized artificial neural network (ANN) model was constructed using test sets divided from MD datasets through error and linear regression analyses. The root mean square error (RMSE) and correlation coefficient (R²) showed the potential of each model in predicting epoxy properties, with high linear correlations (0.835–0.986). This technique can be extended for optimizing the composition of other epoxy resin systems.

Epoxy-based composite materials, widely used in various industries such as coatings, adhesives, aerospace, electronics, and biomedical engineering, remain a topic of global interest due to their varying characteristics based on the base resin and curing agents used. This paper employs molecular dynamics simulation to examine the thermal and mechanical properties, as well as molecular behaviors, of epoxy systems cured with diglycidyl ether of bisphenol F as the base resin and aromatic amine curing agents, specifically the meta structure of 3,3′-diaminodiphenyl sulfone (3,3′-DDS) and the para structure of 4,4′-diaminodiphenyl sulfone (4,4′-DDS). The 3,3′-DDS system demonstrated a greater density and Young’s modulus than the 4,4′-DDS system. This tendency was analyzed based on differences in molecular fractional free volume and cohesive energy density (CED). The 4,4′-DDS system exhibits a higher glass transition temperature (T_g) compared to the 3,3′-DDS system, with values of 406.36 K and 431.22 K, respectively. To understand this behavior, we examined atomic-scale displacements at T_g through mean squared displacement analysis, which revealed that the onset of molecular motion occurs at a lower temperature in the 3,3′-DDS system. Molecular-level study reveals how the structural features of each curing agent appear in thermal and mechanical properties, offering important insights for epoxy system development.

This study explores the CO₂ capture characteristics of hyperbranched poly(ethylenimine) (HB-PEI) and poly(propyleneimine) (HB-PPI) through molecular dynamics simulations using density functional theory-calibrated force fields. Key features such as density, free volume, glass transition temperature, CO₂/H₂O distribution, and molecular diffusion are systematically investigated to elucidate structure-function relationships under dry and hydrated conditions. HB-PEI demonstrates a slightly higher density and lower free volume compared to HB-PPI yet shows superior CO₂ capture due to the high amine concentration. Glass transition analysis indicates a higher thermal mobility in HB-PEI, enhancing the CO₂ diffusivity. Pair correlation and coordination analyses confirm a stronger affinity of CO₂ with primary and secondary amines, particularly in hydrated environments where water competes with CO₂ for binding sites. Despite its more compact structure, HB-PEI outperformed HB-PPI in CO₂ and H₂O transport, as confirmed by higher diffusion coefficients across all hydration levels. These findings highlight a critical balance among polymer architecture, amine accessibility, and hydration in designing next-generation solid amine sorbents for efficient direct air capture applications.

Molecular dynamics simulations were used to investigate the solubility and permeability of H₂O in a self-polishing copolymer (SPC) with two zinc methacrylate (ZMA) contents (Z2: 2 mol% ZMA; Z16: 16 mol% ZMA) and ethyl acrylate, methyl methacrylate, 2-methoxyethyl acrylate, and butyl acrylate as antifouling agents. Water was found to be more soluble in hydrated Z16 than Z2 because ZMA interacts strongly with H₂O. In contrast, the diffusion coefficient of H₂O in Z16 is lower than that of Z2 because H₂O molecules are more constrained in the former due to strong ZMA/H₂O interactions. Z16 was found to be significantly more permeable than Z2 over time. The SPC hydrated region in Z2 tends to expand toward the SPC region, while the analogous region in Z16 swelled toward both the SPC and H₂O regions to leach SPC owing to the higher permeation of H2O into the SPC. These results reveal that H₂O permeability can be controlled by adjusting the ZMA content, which provides insight into antifouling performance.

This study investigates the structural transformation from the γ-phase into the α-phase in bio-based nylon 5,6 fibers during in situ uniaxial stretching, using X-ray diffraction (XRD) and density functional theory (DFT) calculations. Initially, nylon 5,6 films exhibited a well-defined γ-phase crystalline structure, and the as-spun fibers also retained a γ-phase-dominant structure with partial coexistence of α-phase components. Due to the lattice similarity between the γ- and α-phases, phase separation was challenging in the direction perpendicular to the fiber axis (ab-plane). However, the analysis of the (004) diffraction peaks along the fiber axis (c-axis) enabled the quantitative evaluation of each crystalline component. As the stretching progressed, the α(004) peak intensity gradually increased, indicating a continuous γ-to-α structural transition. Furthermore, DFT calculations revealed that the α-phase has lower energy than the γ-phase, supporting the thermodynamic favorability of the phase transition during elongation. These results provide a comprehensive understanding of the crystalline structure and transformation mechanism in environmentally friendly nylon fibers from both experimental and theoretical perspectives, and offer foundational insights for developing nylon materials with desirable properties through the precise control of crystal phase structures.