# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 2 Artificial Intelligence and Machine Learning in the Biomaterial Field
### Inorganic Materials  
The landscape of inorganic materials research is undergoing a revolutionary transformation driven by the integration of ML and AI. Unlike traditional methods, which often rely heavily on trial and error, these advanced computational techniques offer a more efficient and systematic approach to material discovery and optimization. The study presents a machine learning framework designed to predict various properties of inorganic materials, including both crystalline and amorphous structures. The framework utilizes a diverse set of chemical attributes and a unique grouping method to enhance predictive accuracy. The results demonstrate its effectiveness in predicting properties such as band gap energy and glass-forming ability, showcasing the potential of ML in materials science [83]. Dataset's Chemical Diversity Limits the Generalizability of Machine Learning Predictions: Highlighting the limitations of existing datasets like QM9 introduces the PC9 dataset from the PubChemQC project, which offers greater chemical diversity. Testing various ML models, including Kernel ridge regression and neural networks on both datasets revealed that PC9 provided higher energy prediction accuracy, emphasizing the importance of chemical diversity in training data for generalizable ML predictions [84]. Another study combined high-throughput molecular dynamics simulations with machine learning to predict the elastic properties of silicate glasses. The ANN emerged as the most accurate model, while the LASSO algorithm provided a simpler, more interpretable model. The method's versatility allows its application to other properties and systems, \begin{table}
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**Categories** & **Applications** \\ \hline Material design and discovery & Optimization of fiber-matrix combinations: Discovering new fiber and matrix combinations to improve properties like strength and stiffness. Tailoring interfaces: Designing interfaces at the nano and microscale to enhance load transfer and durability. \\ \hline Structural health monitoring & Real-time monitoring: Using ML algorithms to monitor the structural integrity of composites in real time. Damage prediction: Predicting potential failure points and remaining useful life of composite structures. \\ \hline Manufacturing process optimization & Automated lay-up: Optimizing automated lay-up processes for consistent and high-quality composite production. Cure monitoring: Using AI to optimize cure cycles in autoclave and out-of-autoclave processes. \\ \hline Performance enhancement & Lightweighting: Designing lightweight composites for aerospace and automotive applications without compromising strength. Impact resistance: Developing composites with improved impact resistance for use in critical applications. \\ \hline Recycling and sustainability & Recyclability assessment: Assessing and improving the recyclability of composite materials. Sustainable composites: Designing eco-friendly composites with biodegradable or recyclable matrices and fibers. \\ \hline Biomedical applications & Composite implants: Designing composites for load-bearing implants with tailored mechanical properties. Biocompatible composites: Developing composites that are compatible with biological tissues for implants and prosthetics. \\ \hline \hline \end{tabular}
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Table 4: Composite material subfields with ML and AI. illustrating its broad potential in the field [85]. Addressing the challenge of sparse datasets, this research employed Gaussian process regression (GPR) to predict [PERSON]'s modulus in silicate glasses. GPR outperformed neural networks in avoiding overfitting and provided a reliable quantification of prediction accuracy, demonstrating its advanced capability in developing functional materials [86]. Focusing on biomedical glasses used in healthcare, the research introduces an ensemble machine learning model to predict and control the dissolution behavior of these materials in biological fluids.