# Artificial Intelligence in Biomaterials: A Comprehensive Review
## 2 Artificial Intelligence and Machine Learning in the Biomaterial Field
### Metal Materials
Identifying key structural features influencing the mechanical properties of MOFs, researchers harnessed the power of artificial neural networks to expedite the generation of MOFs with desired mechanical properties. This innovative approach aimed to minimize the need for laborious experimental procedures, thereby accelerating the pace of materials discovery and optimization [42]. Utilizing a diverse array of machine learning models, researchers endeavored to predict and optimize the hardness of high entropy alloys. By meticulously analyzing the intricate relationships between alloy composition and mechanical properties, scientists aimed to develop robust predictive models capable of guiding the design and fabrication of high-performance alloys for various applications [52]. Figure 7 shows that by using ML surrogate models to predict properties and uncertainties and employing a DOE utility function to balance exploration and exploitation, the method streamlines the selection and synthesis of promising candidates. Iterative incorporation of new data continuously improves the model's accuracy, while the inclusion of a feature pool in the process allows for more precise training from compositions and physical features. This approach accelerates the development of high entropy alloys by optimizing the experimental process and reducing the time and resources required for material discovery [52]. Employing sophisticated algorithms such as random forest and symbolic regression, researchers gained valuable insights into predicting mechanical properties and phases of amorphous metallic alloys. This comprehensive analysis aimed to elucidate the underlying mechanisms governing the behavior of these complex materials, paving the way for enhanced predictive capabilities and deeper understanding in the field of materials science [53]. A comprehensive review explored the integration of data-driven science, particularly machine learning, to accelerate the design and discovery of MOFs. By synthesizing insights from molecular simulations and ML algorithms, researchers sought to decipher the complex structure-performance relationships inherent in MOFs, offering valuable guidance for future research endeavors in this rapidly evolving field [54]. An ANN model was used to predict water contact angles (WCA) and protein adsorption on self-assembled monolayers (SAMs) based on the chemical structures of SAM molecules. After training the ANN with data from 145 SAMs, it successfully predicted WCA and protein adsorption. This analysis highlighted the importance of various structural parameters for these properties, aiding material design [6]. From optimizing alloy design and additive manufacturing processes to enhancing failure analysis and surface engineering, these technologies have enabled more efficient and precise material development. The integration of ML and DOE-based iterative design loops, as shown in Figure 7, exemplifies how predictive models and experimental optimization can accelerate the discovery and application of high entropy alloys and metal-organic frameworks. These approaches not only streamline the selection and synthesis of promising candidates but also continuously improve through iterative data incorporation.