Gemini for Science
Gemini for Science powers scientific discovery with AI tools and resources built to support scientific endeavors. It brings together experimental tools on Google Labs and science workflows in Google Antigravity to accelerate research, sharpen reasoning, and help researchers explore the future of AI-powered scientific discovery. Literature Insights synthesizes scholarly literature to identify new research opportunities, create grounded research artifacts, and extract paper data into queryable tables mapped directly to source evidence. Hypothesis Generation uses a multi-agent system that simulates the scientific method to identify knowledge gaps, generate potential research directions, and propose testable research plans for breakthrough discoveries. Computational Discovery helps researchers discover models and algorithms by using an agentic research engine that generates and scores code variations based on user-defined optimization metrics.
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SciSure
5.0 out of 5 stars
(4 Ratings)
SciSure helps scientific organizations replace fragmented, disconnected systems with a single governed platform for managing lab operations, so teams spend less time reconciling data across tools and more time on research. By combining Electronic Lab Notebook (ELN), Laboratory Information Management System (LIMS), and Health & Safety (EHS) capabilities in one system, SciSure improves reproducibility, strengthens visibility into day-to-day lab activity, and helps organizations reduce risk as they scale. Researchers document experiments, protocols, and results in a structured, searchable digital format with customizable templates and version control, replacing paper notebooks and scattered files. Samples are tracked from intake through processing, storage, and disposal, maintaining full lineage and chain-of-custody records alongside chemical and general lab inventory management and workflow automation. Health & Safety functionality is built into the same platform, covering chemical inventory alongside Safety Data Sheets (SDS), regulatory compliance workflows, and audit readiness, so labs don't need a separate system to stay compliant and audit-ready.
Labs can also extend the platform through an optional marketplace of add-ons, so they can bring in additional capability, like AI-powered support, automated data analysis, protocol generation, toxicity prediction, and image analysis, only where it adds value, without disrupting the rest of their workflow or committing to features they don't need. SciSure also connects directly to lab instruments, external databases, and third-party tools, so data moves between systems without manual re-entry.
SciSure supports both cloud and dedicated hosting, giving organizations control over the deployment model that fits their infrastructure, security, and compliance requirements. It's built to serve academic institutions, biopharma and biotech companies, and start-ups alike, with role-based support for scientists, lab operations staff, safety and compliance teams, leadership, and IT professionals, so the platform works the way each type of user actually needs it to.
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Hydrogen-1.0
Hydrogen-1.0 is an open-weight scientific language model developed by Deep Principle through post-training GLM-5.3 on chemistry, materials science and life-sciences data, laboratory records and research trajectories. It is intended to help researchers address scientific questions, explain derivations, explore reaction pathways, analyze spectra, plan experiments and develop materials hypotheses. Demonstrations include scientific computing workflows, reaction-network exploration, NMR structure analysis, experimental planning and materials discovery. The model is available as downloadable weights through Hugging Face and ModelScope, through API access on NeuroSpark, and in Deep Principle's Mira AI Scientist. It can also be run locally using compatible hardware and software. Hydrogen-1.0 supports research workflows; users should review its outputs against relevant evidence and scientific constraints.
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Mira
Mira is a web-based AI scientist platform for scientific and materials R&D. It helps researchers review literature, organize evidence, develop hypotheses, plan computational workflows, and summarize results. Task-oriented AI agents combine project context with scientific tools and computation to support work from research question through analysis and reporting. Use cases include chemistry and materials research, molecular design, protein-related workflows, and reproducing published procedures. Project workspaces help retain context across tasks, while workflow views and generated reports let users inspect and organize outputs. Mira is designed for scientists and R&D teams seeking assistance with literature-driven investigation and computational research; its outputs support, rather than replace, expert scientific judgment.
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