Reaction Networks and Data-Driven Chemistry

30 lessons, pages 4341–4370.

  1. Chemical Reactions as Networks — Representing species, reactions and pathways as connected structures
  2. Stoichiometric Matrices — Encoding reactant and product coefficients for a reaction system
  3. Conservation Laws in a Network — Element, charge and moiety balances as constraints on reaction models
  4. Elementary-Step Rate Laws — Converting a proposed mechanism into concentration-dependent fluxes
  5. Ordinary Differential Equation Models — Predicting species concentrations from stoichiometry and kinetic rates
  6. Steady States and Flux Balance — When production and consumption of intermediates cancel
  7. Parallel and Sequential Pathways — Selectivity and time profiles in competing reaction schemes
  8. Reversible Reaction Networks — Forward and reverse rates, equilibrium constants and detailed balance
  9. Thermodynamic Consistency of a Network — Constraining cycle free energies and equilibrium ratios
  10. Timescale Separation — Fast pre-equilibria and quasi-steady-state approximations
  11. Sensitivity Analysis — How predictions respond to uncertain rate constants or starting conditions
  12. Parameter Estimation — Fitting kinetic models while respecting units and measurement error
  13. Identifiability of Mechanisms — Why different pathways can fit the same limited data
  14. Uncertainty in Kinetic Parameters — Confidence regions, correlated parameters and prediction intervals
  15. Experimental Design for Mechanism Testing — Choosing concentrations, temperatures and sampling times that distinguish models
  16. Transient Kinetics — Perturbation experiments that reveal intermediates and timescales
  17. Reaction Graph Search — Exploring plausible elementary transformations without losing chemical constraints
  18. Automated Mechanism Generation — Rule-based candidate reactions, pruning and validation
  19. Energy Barriers in a Reaction Network — Connecting computed stationary points to temperature-dependent rate constants
  20. Catalytic Reaction Networks — Site balances, adsorbate coverages and turnover pathways
  21. Atmospheric Chemistry Networks — Radical chains, photolysis and spatially varying conditions
  22. Combustion and Chain-Branching Networks — Radical propagation, ignition and model reduction
  23. Biochemical Network Models — Enzyme fluxes, metabolite pools and regulated pathways
  24. Reaction Network Reduction — Removing low-impact steps while retaining the predictions of interest
  25. Data Quality for Chemical Machine Learning — Provenance, duplicate leakage, inconsistent units and missing negative results
  26. Molecular and Reaction Representations — Graphs, fingerprints and chemically valid feature construction
  27. Training and Testing Chemical Predictors — Splits, baselines, distribution shift and honest evaluation
  28. Uncertainty-Aware Reaction Prediction — Calibrating probabilities and recognizing out-of-domain chemistry
  29. Combining Mechanistic and Data-Driven Models — Using physical constraints and observations together without hiding assumptions
  30. Reaction Networks and Data: Unit Review — Integrating stoichiometry, kinetics, inference and model validation