Climate Change and Materials Criticality challenges are driving urgent responses from global governments. These global responses drive policy to achieve sustainable, resilient, clean solutions with Advanced Materials (AdMats) for industrial supply chains and economic prosperity. The research landscape comprising industry, academe, and government identified a critical path to accelerate the Green Transition far beyond slow conventional research through Digital Technologies that harness Artificial Intelligence, Smart Automation and High Performance Computing through Materials Acceleration Platforms, MAPs. In this perspective, following the short paper, a broad overview about the challenges addressed, existing projects and building blocks of MAPs will be provided while concluding with a review of the remaining gaps and measures to overcome them.

Two key issues hindering the transition toward findable, accessible, interoperable, and reusable (FAIR) data science are the poor discoverability and inconsistent instructions for the use of data extractor tools (i.e., how we go from raw data files created by instruments, to accessible metadata and scientific insight). If the existing format conversion tools are hard to find, install, and use, their reimplementation will lead to a duplication of effort and an increase in the associated maintenance burden is inevitable. The framework presented in this article addresses these issues. First, by providing a curated registry of such extractor tools, their discoverability will increase. Second, by describing them using a standardized but lightweight schema, their installation and use are machine-actionable. Finally, we provide a reference implementation for such data extraction. The framework can be used to provide a public-facing data extraction service, or be incorporated into other research data management tools providing added value. Impact statement: The ongoing digitalization of materials research necessitates investment in bespoke research data management platforms. A common pain point across such infrastructures is the ingestion and extraction of data and metadata from raw data files. Often these files require significant effort to ingest in a way consistent with the FAIR data principles such that definitions are clear and the resulting data are extracted reliably and repeatedly. As research laboratories increasingly move toward coupled automated systems, handling of files exported by different instruments can introduce significant friction to the data-to-insight pipeline. This paper presents a conceptual framework and prototype implementation of Datatractor, which aims to bring together the community of efforts on open-source software tools that can perform such extractions and disseminate them in such a way that new platforms can use the tools in an automated fashion. This can reduce the duplication of effort by broadly advertising existing and tested tools, communalize the maintenance and development burden of such tools, and lower the barrier to the creation of materials data infrastructure. By building atop community consensus formed in a Materials Research Data Alliance (MaRDA) Working Group, it is hoped that this framework can grow to propagate best practices in research data management and help to foster a rich open data ecosystem in materials science and beyond. Graphical

Smart materials usually are either mounted on surfaces or embedded inside; in this work, we study magnetic properties of smart materials that are integrated into the structure. Magnetostrictive particles of Tb_xDy_1−xFe₂ (x ∼ 0.3), commonly known as Terfenol-D, incorporated into carbon fiber reinforced polymers showed a 3000% enhancement in the coercive field compared to the reference pristine bulk Terfenol-D. Torque magnetometer measurements revealed a magnetic anisotropy with twofold symmetry. The lowest applied field at which a reversible torque without hysteresis occurred was ∼5.0 kOe, which yields 5 Oe anisotropic field for a carbon fiber reinforced polymer magnet. This value of the anisotropic field is confirmed using the Miyajima method. Angle dependent coercive field and saturation/remanent magnetization also confirmed the twofold symmetry of magnetization in this system. The high magnetostrictive coefficient, together with the enhanced coercive field, makes Terfenol-D integrated into carbon fiber reinforced polymers serve as an excellent candidate for in situ sensing of damage in various polymer composites under several mechanical loading conditions.

This article presents the first use of shape forming elements (SFEs) to produce architected composites from multiple materials in an extrusion process. Each SFE contains a matrix of flow channels connecting input and output ports, where materials are routed between corresponding ports. The mathematical operations of rotation and shifting are described, and design automation is explored using Bayesian optimization and genetic algorithms to select fifty or more parameters for minimizing two objective functions. The first objective aims to match a target cross-section by minimizing the pixel-by-pixel error, which is weighted with the structural similarity index (SSIM). The second objective seeks to maximize information content by minimizing the SSIM relative to a white image. Satisfactory designs are achieved with better objective function values observed in rectangular rather than square flow channels. Validation extrusion of modeling clay demonstrates that while SFEs impose complex material transformations, they do not achieve the material distributions predicted by the digital model. Using the SSIM for results comparison, initial stages yielded SSIM values near 0.8 between design and simulation, indicating a good initial match. However, the control of material processing tended to decline with successive SFE processing with the SSIM of the extruded output dropping to 0.023 relative to the design intent. Flow simulations more closely replicated the observed structures with SSIM values around 0.4 but also failed to predict the intended cross-sections. The evaluation highlights the need for advanced modeling techniques to enhance the predictive accuracy and functionality of SFEs for biomedical, energy storage, and structural applications.

Model-guided compatibilization of post-consumer rPP/rPE blend enables smooth film extrusion with simultaneous gains in strength and ductility.