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AbstractManaging, processing, and sharing research data and experimental context produced on modern scientific instrumentation all present challenges to the materials research community. To address these issues, two MaRDA Working Groups on FAIR Data in Materials Microscopy Metadata and Materials Laboratory Information Management Systems (LIMS) convened and generated recommended best practices regarding data handling in the materials research community. Overall, the Microscopy Metadata Group recommends (1) instruments should capture comprehensive metadata about operators, specimens/samples, instrument conditions, and data formation; and (2) microscopy data and metadata should use standardized vocabularies and community standard identifiers. The LIMS Group produced the following guides and recommendations: (1) a cost and benefit comparison when implementing LIMS; (2) summaries of prerequisite requirements, capabilities, and roles of LIMS stakeholders; and (3) a review of metadata schemas and information-storage best practices in LIMS. Together, the groups hope these recommendations will accelerate breakthrough scientific discoveries via FAIR data. Impact statementWith the deluge of data produced in today’s materials research laboratories, it is critical that researchers stay abreast of developments in modern research data management, particularly as it relates to the international effort to make data more FAIR – findable, accessible, interoperable, and reusable. Most crucially, being able to responsibly share research data is a foundational means to increase progress on the materials research problems of high importance to science and society. Operational data management and accessibility are pivotal in accelerating innovation in materials science and engineering and to address mounting challenges facing our world, but the materials research community generally lags behind its cognate disciplines in these areas. To address this issue, the Materials Research Coordination Network (MaRCN) convened two working groups comprised of experts from across the materials data landscape in order to make recommendations to the community related to improvements in materials microscopy metadata standards and the use of Laboratory Information Management Systems (LIMS) in materials research. This manuscript contains a set of recommendations from the working groups and reflects the culmination of their 18-month efforts, with the hope of promoting discussion and reflection within the broader materials research community in these areas. Graphical abstract 

We have developed an image-based convolutional neural network that is applicable for quantitative time-resolved measurements of the fragmentation behavior of opaque brittle materials using ultra-high speed optical imaging. This model extends previous work on the U-net model. Here we trained binary-, three-, and five-class models using supervised learning on experimentally measured dynamic fracture experiments on various opaque structural ceramic materials that were adhered on transparent polymer (polycarbonate or acrylic) backing materials. Full details of the experimental investigations are outside the scope of this manuscript, but briefly, several different ceramics were loaded using spatially and time-varying mechanical loads to induce inelastic deformation and fracture processes that were recorded at frequencies as high as 5 MHz using high-speed optical imaging. These experiments provided a rich and diverse dataset that includes many of the common fracture modes found in static and dynamic fractures, including cone cracking, median cracking, comminution, and combined complex failure modes that involve effectively simultaneous activation and propagation of multiple fragmentation modes. While the training data presented here were obtained from dynamic fragmentation experiments, this study is applicable to static loading of these materials as the crack speeds are on the order of 1–10 km/s regardless of the loading rate. We believe the methodologies presented here will be useful in quantifying the failure processes in structural materials for protection applications and can be used for direct validation of engineering models used in design. 

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.