Research data management, experiment design, lab productivity, and news from the MatBook team.
Paper notebooks can no longer keep pace with modern materials science. Discover how electronic lab notebooks built for materials workflows are improving reproducibility, enabling collaboration, and laying the groundwork for AI-driven discovery.
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A well-designed materials database transforms fragmented experimental data into a searchable, reusable knowledge asset. Learn the data modeling, provenance, and automation principles that separate successful database projects from those that stall.
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Graph neural networks and Bayesian optimization are compressing the materials discovery timeline from decades to months. Explore how ML is screening compositional space, predicting properties, and beginning to generate novel materials from scratch.
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More than 70% of researchers report failing to reproduce another scientist's experiments. Materials science faces distinctive reproducibility challenges tied to processing sensitivity and underspecified methods. Learn how structured data capture can help.
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XRD diffractometers, SEMs, thermal analyzers — each generates data in its own proprietary format, stored in isolation. Explore the integration architectures that connect instrument data to your experiment records automatically and reliably.
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Research data management overhead consumes up to 30% of researcher time. Learn how to quantify the time savings, risk reduction, and grant competitiveness value of RDM investment — and how to build a compelling case for your institution.
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Materials science depends on collaboration, yet most research groups exchange data through email attachments and shared drives that strip away context. Discover how shared data infrastructure, protocol harmonization, and access control enable genuine scientific collaboration.
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Data organization and preparation for manuscript submission consumes 60–120 researcher-hours per paper. Learn how structured digital records, figure-to-data linkage, and automated metadata generation transform the publication pipeline.
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Open data mandates and community databases promise to accelerate materials discovery, but data quality gaps, IP concerns, and incentive misalignment complicate the picture. A frank assessment of where the open science movement stands in materials research.
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Foundation models, self-driving laboratories, and uncertainty-aware property prediction are transforming what is possible in materials research. Explore the current state of materials informatics and the infrastructure investments needed to benefit from its coming capabilities.
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