NNSA Genesis 01 ArrheniusAI
Temperature-driven reaction analogs to accelerate unclassified AI-assisted materials and energy reasoning.
Informational technical studies prepared in response to a DOE/NNSA Request for Information.
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Twenty-two studies with one-line summaries and direct access.
Temperature-driven reaction analogs to accelerate unclassified AI-assisted materials and energy reasoning.
Agentic folding heuristics for decomposing complex national-security design spaces into reviewable modules.
Fluid-dynamics-inspired operators to stabilize large-scale AI inference and simulation workflows.
Logistic dynamics to bound emergent behavior and failure modes in autonomous decision systems.
Laplacian operators mapped to HPC scheduling to improve convergence, throughput, and utilization.
Bayesian uncertainty quantification for rapid early-stage risk assessment and decision support.
Cross-entropy methods for efficient fine-tuning under constrained data and governance regimes.
Minimax agent strategies for adversarial, game-theoretic, and red-team/blue-team environments.
Gibbs sampling analogs for scalable probabilistic reasoning and posterior exploration.
Fracture-mechanics-inspired resilience modeling for cascading failures in complex systems.
Entropy metrics to stress-test AI-enabled supply-chain robustness and brittleness.
Generative explorer for hypothesis discovery across heterogeneous, unclassified datasets.
Markov hybrid coordination for mixed deterministic–stochastic analytical pipelines.
Iterative safety loops for AI validation, review, and bounded deployment in high-consequence settings.
Unclassified monitoring concepts for nonproliferation using AI pattern synthesis and proxy signals.
Enrichment-signature detection approaches using open proxies and disciplined inference workflows.
Agentic analysis patterns for multi-step reasoning under uncertainty with auditable checkpoints.
Data-fusion architectures for integrating disparate unclassified signals into coherent assessments.
Consortia governance models for cross-institution coordination, standards, and oversight.
Cost and pricing structures for scalable AI deployment decisions under real-world constraints.
AI data center campus modeling using nuclear baseload power proxies for sovereign energy resilience, capacity planning, and grid independence.
Deterministic post-generation validation layer scoring factuality, semantic consistency, discourse coherence, and tone to support defense-in-depth AI assurance in high-assurance environments.
Strategic, planning-only foresight frameworks exploring how multiple DOE-funded recycling pathways could operate as a reversible and safeguardable U.S. fuel-cycle ecosystem.
An executive, reversible integration architecture that connects five DOE-funded recycling pathways into one decision-ready foresight model—without committing to implementation.
A strategic conversion pathway showing how legacy research reactor fuel could be transformed into HALEU to strengthen domestic advanced-reactor fuel security.
A modular planning framework examining how electrochemical processing could enable high-efficiency recovery while preserving reversibility and safeguards integrity.
A conceptual pathway exploring molten-salt deposition methods to optimize advanced pyroprocessing performance and next-generation fuel-cycle design.
A modernization concept evaluating how recovered uranium could be converted into enrichment-ready UF₆, supporting resilient domestic fuel supply chains.
A lifecycle integration construct linking transport, storage, disposal, and hydro-processing into a unified, reversible waste-management planning architecture.
This initiative stands on its own. Readers interested in related DOE planning efforts may optionally review a brief contextual overview here.