Open Arsenal

Mapping how science actually happens.

TraceGraph OS is a scientific intelligence operating system designed to model and preserve the full lifecycle of discovery. Instead of treating research outputs as isolated results, it structures science as a continuously evolving system of hypotheses, experiments, simulations, and evidence. Every step is tracked through provenance-aware workflows, ensuring that users can trace not only what was discovered, but exactly how and why it was discovered.

At its core, TraceGraph OS combines AI-driven materials discovery with a deep scientific memory graph that captures causal relationships, temporal evolution, and methodological lineage. The system integrates polymer knowledge bases, property prediction engines, and multi-scale simulation tools with optimization frameworks that support inverse design and hypothesis generation. This allows researchers to move seamlessly from desired material properties to candidate structures, while maintaining full traceability of all assumptions, models, and datasets involved.

A defining feature of TraceGraph OS is its Sources and Methods system, which enforces strict reproducibility by recording datasets, literature, experimental protocols, model versions, and computational environments for every result. This is extended by a Temporal Science Engine that models how materials behave and degrade over time, and a Regulatory and Standards Engine that evaluates compliance with industrial, environmental, and safety frameworks.

The platform also includes a Safety, Ethics, and Misuse Filter that actively screens outputs for hazardous, unethical, or dual-use risks, ensuring that generative capabilities remain bounded by scientific and regulatory constraints. Complementing this is a Failure Intelligence System that systematically learns from failed experiments, turning negative results into structured knowledge that improves future predictions.

Finally, TraceGraph OS is built for collaboration and scalability. Its Human-AI Interface Layer enables researchers to co-develop hypotheses and experiments with AI agents, while its multi-agent architecture distributes tasks across specialized systems for simulation, optimization, validation, and analysis. Together, these components form a unified environment where scientific discovery is not only accelerated, but made fully transparent, reproducible, and historically traceable.

This specification is released under the GNU Affero General Public License v3.0 or later (AGPL-3.0+) and may be used freely with required attribution under Section 7. A Specification Branding License is available for attribution-free deployments, with fees based on usage, scope, and deployment size.