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This paper introduces MolFoundry, a graph transformation framework designed to automate observable closure analysis for dynamic systems. By composing rules with nested application conditions and leveraging rule-algebraic stochastic rewriting, the framework avoids explicit state-space enumeration. The key result is the generation of closed ordinary differential equations or finite event models for average pattern counts, enabling quantitative predictions directly from molecular graph rules.
Automated ODEs for molecular dynamics emerge directly from graph transformation rules, bypassing infinite state spaces.
Graph transformations model changing systems, with applications in software, biology and chemistry, among others. We introduce MolFoundry, a graph transformation framework, and present a workflow for observable analysis that tracks the dynamics of local patterns through rule composition. Analysing rule compositions avoids the need to enumerate a potentially infinite state space. Building on rule-algebraic stochastic rewriting, MolFoundry's kernel composes rules with nested application conditions, including missing-edge constraints. Given rules, rates and query patterns, the algorithms iteratively identify auxiliary pattern counts and search for closure. Using exact counting identities supplied by graph-family backends, successful searches yield closed ordinary differential equations for average counts or, with justified individual jumps, finite event models describing joint changes and rates. We evaluate conditional composition and automated discovery of auxiliary observables against published calculations and independent rule application. Applications to aldol addition, copolymerization and chlorination show how the framework yields quantitative predictions directly from molecular graph rules.