Year: 2026
Type: preprint
Abstract: Sequence-controlled polymers hold immense potential for advanced materials design, yet discovering their experimental recipes is bottlenecked by the high-dimension kinetic parameter space of multicomponent copolymerization. Traditional ordinary differential equation (ODE) solvers provide rapid macrokinetic predictions but are fundamentally blind to explicit backbone microstructures. To bridge this gap, we use a stochastic simulation algorithm (SSA) engine to generate high-throughput terpolymer sequence data from a physically constrained parametric sweep across a 9-dimensional kinetic space, charting how feed stoichiometry and kinetic propensities dictate macromolecular structure. Information-theoretic analysis and t-SNE dimensionality reduction of the resulting 27-component triad frequency matrices for each simulation reveal profound microstructural degeneracies, demonstrating that traditional ensemble descriptors, such as the Harwood run number, are fundamentally incapable of describing sequence specifics. To accelerate optimization, we establish a digital twin strategy utilizing an XGBoost regressor to instantaneously predict localized chain-end propagation probabilities based on target sequence motifs. Finally, we demonstrate a closed-loop inverse design strategy that passes the 2 machine learning predictions to a deterministic root-finding optimizer, isolating exact initial monomer feeds and reactivity ratios needed to create them. By training an ML surrogate using explicit chain sequences from SSA, this framework provides a powerful alternative to classical macrokinetic equations, establishing an immediate, predictive tool to accelerate the discovery and reliable synthesis of sequence-controlled functional macromolecules.
Source: ChemRxiv
Author: Jeffrey C. Foster
Institution: Oak Ridge National Laboratory
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7220431536