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Abstract Programmable metamaterials have seen increased interest in recent years due to their suitability for a wide range of applications along with the high level of control that they offer over their structural properties. In particular, significant interest has been generated for their application in vibration control as they can be updated and tuned without having to completely rebuild an entire section. However, their complex behavior can make modelling them difficult and time consuming. In recent years, machine learning has emerged as a powerful tool to predict behavior of metamaterials and help reduce the amount of testing time. While prior work demonstrated the efficacy of machine learning on metamaterials with fewer permutations, minimal focus has been placed on its applications for large datasets. This work aims to bridge this gap and demonstrate the possibility of using machine learning algorithms to predict complex metamaterial behavior which is trained on a relatively small dataset. Discussion is also given to a novel approach to collecting experimental data for similar applications. 

Abstract Hydrogels with particulates, including proteins, drugs, nanoparticles, and cells, enable the development of new and innovative biomaterials. Precise control of the spatial distribution of these particulates is crucial to produce advanced biomaterials. Thus, there is a high demand for manufacturing methods for particle-laden hydrogels. In this context, 3D printing of hydrogels is emerging as a promising method to create numerous innovative biomaterials. Among the 3D printing methods, inkjet printing, so-called drop-on-demand (DOD) printing, stands out for its ability to construct biomaterials with superior spatial resolutions. However, its printing processes are still designed by trial and error due to a limited understanding of the ink behavior during the printing processes. This review discusses the current understanding of transport processes and hydrogel behaviors during inkjet printing for particulate-laden hydrogels. Specifically, we review the transport processes of water and particulates within hydrogel during ink formulation, jetting, and curing. Additionally, we examine current inkjet printing applications in fabricating engineered tissues, drug delivery devices, and advanced bioelectronics components. Finally, the challenges and opportunities for next-generation inkjet printing are also discussed. Graphical Abstract