The Technology
Predictive chemical kinetic modeling is essential to industries such as sustainable fuels, energy systems, environmental chemistry, pharmaceuticals, and advanced materials, yet the market standard today relies on automated but fundamentally static workflows that assemble reaction mechanisms, estimate thermochemistry and rate coefficients, and validate models using tools like RMG, Genesys, ARC, AutoTST, and Cantera; although powerful, these pipelines cannot independently adapt to new data, diagnose model–experiment disagreement, or plan revisions, leaving researchers with a labor-intensive and slow path toward reliable predictive models.
The new invention replaces this rigidity with a dual-lane architecture that introduces an “AI Chemist”—an agentic AI orchestration layer that plans, reasons, and dynamically coordinates the entire model-development process under explicit uncertainty, cost, and safety constraints.
While the automated lane executes validated tasks such as mechanism generation, parameter refinement, and uncertainty quantification, the AI Chemist continuously evaluates model performance, identifies disagreement signals, prioritizes revisions by expected uncertainty reduction per unit cost, integrates literature through Retrieval-Augmented Generation, proposes targeted experiments, and routes high-impact actions through human-in-the-loop approval.
This AI Chemist transforms kinetic model development from a static, manually orchestrated workflow into a goal-directed, adaptive, and fully auditable process that rapidly converges toward decision-grade predictive models—those with validated uncertainty, reproducibility, and complete provenance.
Advantages
• Produces decision-grade models with validated uncertainty and full provenance
• Accelerates development by optimizing actions for maximum uncertainty reduction per cost
• Adapts dynamically to new data, discrepancies, and evolving constraints
• Reduces manual iteration and computation waste through intelligent planning
• Enhances safety and governance with human-in-the-loop gates and budget controls
• Integrates seamlessly with existing kinetic modeling and quantum chemistry toolchains
Applications and Opportunities
• Sustainable aviation fuel design, combustion modeling, and emissions prediction
• Chemical manufacturing, reactor optimization, and process safety analysis
• Atmospheric chemistry and environmental modeling with uncertainty-aware predictions
• Pharmaceutical and materials research involving mechanism discovery and synthesis planning
• Autonomous or self-driving laboratories where the AI Chemist runs closed-loop experiment–model optimization
Business Development Contacts
