# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 2 Artificial Intelligence and Machine Learning in the Biomaterial Field
### Composite Materials  
The intersection of composite materials and advanced computational techniques like AI and ML is reshaping the landscape of materials science. Unlike traditional methods, which often rely on time-consuming and costly experimental procedures, ML and AI offer a streamlined approach to discovering and optimizing new composites. Researchers can predict material behaviors, uncover new material combinations, and enhance properties with unprecedented accuracy and speed by using a vast number of datasets and sophisticated algorithms. This technological fusion not only accelerates the development cycle but also opens up new possibilities for creating high-performance composites that meet specific, demanding criteria across various industries. One of the studies delved into the creation of statistical models to capture elastic localization relationships in high-contrast composite materials. By systematically evaluating various feature selection and regression approaches, the research concluded that combining basic and engineered feature descriptors, particularly through ensemble methods like random forests, markedly improves prediction performance and efficiency [70]. Another investigation introduced a 3D convolutional neural network (CNN) to model elastic homogenization in high-contrast composites. Figure 10 presents a schematic illustration of a sample 3D CNN configuration. This deep learning approach significantly outperformed traditional physics-inspired methods, reducing the mean absolute scaled error (MASE) by up to 54% [71] The study highlighted the potential of advanced CNN architectures and regularization techniques to further enhance model performance, emphasizing deep learning's transformative impact on materials science. Researchers proposed a bioinspired approach to hierarchical composite design by integrating machine learning with finite element analysis. This model accurately predicted mechanical properties and generated new microstructural patterns, validated through additive manufacturing and tensile testing. This method demonstrated machine learning's capability to significantly speed up the exploration and optimization of high-performing materials [72]. An innovative AI-based method was introduced for optimizing the design of 2D nanocomposites for shear crack resistance. In the study, the proposed combined a deep CNN with a global optimizer to explore the design space efficiently, showcasing the AI tool's ability to predict and design composites resilient to various fracture conditions, thus proving its versatility and scalability [73]. The training set was split into 90% for training and 10% for validation to prevent overfitting, with the loss function showing significant initial decreases and converging around 10\({}^{-5}\) after 1000 epochs, as depicted in Figure 11b. The training process flowchart is shown in Figure 11a, and the regression values for toughness and strength are presented in Figure 11c,d, respectively. Figure 10: Example architecture of a 3D convolutional neural network. The figure is reproduced from [71]. In a study focused on high-entropy alloys (HEAs), machine learning, particularly ANNs, was employed to predict and optimize the composition of HEAs for maximum hardness. Integrating simulated annealing with the ANN, the study successfully discovered novel HEA compositions with superior hardness, surpassing previously reported values, thus illustrating the potential of ML in alloy design [74]. Another study utilized AI to predict fracture patterns in crystalline solids, demonstrating the potential for generative methods to support reverse engineering of mechanical properties. The study suggested integrating adversarial training and advanced neural networks to design crack-insensitive materials, adaptable to various force fields and material properties [75]. Additionally, [PERSON] et al.