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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 

Large Language Models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 34 total projects developed during the second annual Large Language Model Hackathon for Applications in Materials Science and Chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility. 

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.