# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 7 Computational and Intelligent Method-Driven Development of Epoxy Resins
### Machine Learning  
With the rapid advancement of data science, machine learning (ML) techniques [130; 131] have emerged as powerful tools for predicting the thermomechanical properties and fracture characteristics of epoxy composites. Machine learning models trained on experimental or simulated datasets can be utilized for virtual screening of epoxy formulations, optimization of composite designs, and accelerated prediction of key performance metrics including fracture toughness, modulus, and glass transition temperature [132; 133; 134].  
[PERSON] et al. [135] proposed a machine-learning-enabled materials genome approach (MGA) for rapid design of epoxy resin (EP) polymers with high modulus, high strength, and high toughness. By integrating attention-enhanced and gating-enhanced graph convolutional networks (GCN+a+g), multi-layer perceptrons (MLPs), classical gel theory, and transfer learning from small molecules to polymers, they developed predictive models to accurately forecast the mechanical properties of EPs. As illustrated in Figure 9, high-throughput screening across extensive chemical spaces successfully identified a series of EP candidates with outstanding comprehensive mechanical properties, whose performance was experimentally validated. Furthermore, critical genetic substructures influencing polymer mechanical behaviors were systematically analyzed, unveiling the microscopic mechanisms underlying high-performance EP design.  
[PERSON] et al. [136] established a predictive framework integrating molecular dynamics (MD) simulations and machine learning methodologies to accurately determine key physicochemical properties of epoxy resins, including density, coefficient of thermal expansion (CTE), glass transition temperature (Tg), and Young's modulus. This methodology com  
Figure 9: MGA High-Throughput Screening (MGA-HTS) [135].  
menced with the generation of 789 distinct epoxy resin configurations via MD simulations, followed by the extraction of molecular descriptors as input features for training machine learning models. The results demonstrated strong predictive accuracy across multiple material properties, highlighting the framework's potential for accelerating rational material design and high-throughput screening of advanced epoxy systems.  
[PERSON] et al. [137] proposed a hybrid methodology integrating molecular dynamics (MD) simulations and machine learning (ML) for the rational design of high-performance epoxy resin systems. By generating training datasets through MD simulations and subsequently employing ML to predict the effects of compositional variations on material performance, this approach enabled systematic optimization of epoxy formulations. The study identified critical roles of key components: 4_A_\({}^{\prime}\)-diaminodiphenyl sulfone (DDS) as a curing agent significantly enhanced glass transition temperature (Tg), Young's modulus (E), and elongation at break (\(\delta\)), while tetraglycidyl diamino diphenylmethane (TGDDM) ensured high Tg, E, and ultimate tensile strength (UTS). To further improve toughness, controlled incorporation of diglycidyl ether of bisphenol A (DGEBA) proved essential. Additionally, polyether sulfone (PES) demonstrated effectiveness as a toughening agent, enhancing fracture resistance without compromising modulus. The optimized epoxy system designed via this methodology achieved simultaneous performance enhancements: 17.3% increase in Tg, 15% improvement in E, 32.7% elevation in UTS, and 85.5% gain in \(\delta\), while maintaining balanced thermomechanical properties.