TL;DR
This Review surveys machine learning methods, including surrogate modelling, generative design and active learning loops, for predicting and optimizing properties across metals, polymers, hydrogels, ceramics and biological biomaterials, and outlines key datasets, challenges, and future autonomous discovery pipelines.
3D (bio)printing is increasingly used for scaffold fabrication in tissue engineering; however, identifying suitable biomaterial compositions, cell types, cell densities, and printing conditions remains largely empirical, time-consuming, and costly. Practical application of machine learning in this field is further limi...
ABSTRACT Bone defects require materials with osteogenic, neurogenic, and angiogenic activity, yet designing such materials within high‐dimensional compositional spaces remains challenging. Here, we report a machine learning (ML) driven strategy to accelerate the design of multicomponent mesoporous bioactive glasses (MB...
Biomaterials are indispensable to modern healthcare, yet their development remains hindered by traditional trial-and-error approaches that are costly, time-intensive, and inefficient. Artificial intelligence (AI) offers a powerful alternative, but most existing reviews treat AI merely as a computational tool for proper...
ABSTRACT 3D bioprinting is known for its high precision and reproducibility in fabricating complex and customized biomedical constructs. However, its applications are limited by their static nature; i.e., unlike native tissues, they cannot change shape or functionality over time. To overcome this, 4D bioprinting has em...
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