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Area of Science:

  • Computational Chemistry
  • Chemical Reaction Networks
  • Complexity Theory

Background:

  • Modeling complex chemical reactions is challenged by high-dimensional potential energy surfaces (PESs).
  • Accurate PES modeling is vital for understanding diverse chemical processes like organocatalysis and molecular assembly.
  • Prior work established low-dimensional reaction network representations via PES discretization.

Purpose of the Study:

  • To investigate the inherent structure within stoichiometry-preserving reaction networks derived from PES discretization.
  • To determine if these networks exhibit regular lattice-like properties with minimal randomness.
  • To quantify the dimensionality reduction achieved and its implications for computational methods.

Main Methods:

  • Discretized potential energy surfaces (PESs) into reaction networks using bond-breaking/formation heuristics.
  • Compared CHNO reaction networks against various generative network models (random, lattice, Watts-Strogatz).
  • Analyzed local, metric, and global network properties to identify structural similarities.

Main Results:

  • Stoichiometry-preserving reaction networks exhibit approximate regular lattice structures with minor edge rewiring.
  • A nonlinear dimensionality reduction by a factor of 10 was achieved, quantified by an error measure.
  • CHNO networks closely matched 3-4 dimensional lattices with ≤10% random edge rewiring across all analyzed properties.

Conclusions:

  • Heuristics-based PES discretization effectively reduces dimensionality and reveals underlying network structure.
  • The observed lattice structure is robust, independent of system size, stoichiometry, and ruleset.
  • This finding supports the leveraging of search and sampling algorithms for complex reaction PES exploration.