# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 2 Artificial Intelligence and Machine Learning in the Biomaterial Field
### Polymer Materials  
The integration of ML with biomaterial research has significantly advanced the design and discovery of novel polymer-based biomaterials. One notable study utilized LAD regression to analyze and predict polymers for safe and effective gene delivery. This was achieved by conducting a large-scale series of 12,000 transfection experiments based on the Michael addition polymerization of acrylates with amines [55]. QSAR models have also been employed to predict protein adsorption and cellular response on biodegradable polymers designed for tissue engineering applications. These models used descriptors calculated through molecular operating environments or Dragon and were optimized using artificial neural networks [56]. High-throughput methods have played a crucial role in understanding the structure-function relationships of polymers. For instance, ToF SIMS analysis was correlated with cell adhesion properties using PLS regression, highlighting the importance of surface functionalities in controlling human embryoid body cell adhesion [57]. Additionally, a collection of 496 polyacrylate microarrays was created to investigate the effects of hydrophobicity, hydrophilicity, and crosslinking density on polymer properties and their influence on cellular responses [58]. To further understand the interactions between polymer properties and cellular behaviors, the nonlinear Bayesian neural network model, BRANNGP, was employed to study the correlation between human embryonic stem cell (hES) adhesion and the attributes of polyacrylate surfaces [59]. Over the past decade, significant progress has been made in  
Figure 7: A schematic of the iterative design loop utilizing ML and design of experiments (DOE) for the accelerated development of high-entropy alloys (HEAs). The figure is reproduced from [52]. using polymers as scaffolds in tissue engineering to mimic the functions of natural tissues and create artificial tissues and organ substitutes [60]  
The Polymer Genome Project exemplifies the integration of high-throughput computation, targeted experimentation, and ML to design novel polymer dielectrics for capacitive energy storage. This project has resulted in the creation of an online knowledgebase that stores accumulated data and prediction models, facilitating efficient material selection and design, as illustrated in Figure 8 [61]. Additionally, Bayesian network and ML techniques have been applied to identify key factors influencing the growth of small cracks in polycrystalline materials, combining high-resolution experiments with crystal plasticity simulations to reliably predict crack propagation [62]. The design of self-assembling dipeptide hydrogels has been enhanced by ML, which efficiently predicts hydrogels' physical characteristics. This combinatorial approach has shown potential in various biomedical applications, such as drug delivery and tissue engineering [63]. Melt electrowriting (MEW) has been employed to create 3D substrates with customizable porous structures, which can influence cell morphology and control cell phenotype at the single-cell level. Additionally, a machine-learning-based metrology framework has been developed to investigate how these substrate architectures affect cell shape and the distribution of focal adhesion proteins [64]. Data-driven approaches have also been applied to the study of organic photovoltaic polymers. Methods such as self-organizing mapping and tailored polymer descriptor generators have identified key factors like photon energy loss and the number of fluorine atoms, correlating strongly with power conversion efficiency [65]. As shown in Figure 9 the data processing pipeline begins with a preprocessed dataset, where relevant features are selected, and input data include SMILES notation and other molecular properties. These inputs form the training set for self-organizing maps (SOM), which are then trained and validated. A molecular descriptor generator (MDG) creates molecular descriptors, and quality measurements assess model performance.