Predictive Modeling of Chemical Reactivity for Sustainability

Abstract

Predicting and controlling chemical reactivity is key to sustainable material and process design. However, modeling reactivity at scale remains challenging due to the computational demands of quantum chemical methods and the complexity of reaction mechanisms. This thesis explores how high-throughput computational approaches, rooted in quantum chemistry and enabled by automation, can be used to interrogate reactivity across large chemical spaces. We focus on two domains where reactivity governs process efficiency and sustainability: solvent-based carbon capture and polymer, specifically thermoset, manufacturing.

We first investigate pi-conjugated heterocyclic nucleophiles as alternative carbon capture solvents to address the high regeneration energy and degradation rates of conventional amine-based systems. We combine synthetic template-based library enumeration, density functional theory (DFT), and machine learning models to evaluate binding energies, capture capacity, regeneration thermodynamics, and oxidative stability. Structure–property analysis reveals design strategies to enhance capture strength while balancing tradeoffs with desorption temperature and degradation resistance.

We next focus on designing monomers for frontal ring-opening metathesis polymerization (FROMP), a polymerization mode that enables rapid, energy-efficient manufacturing of polymers. This self-propagating process harnesses exothermic reactions to sustain a polymerization front without continuous external heating, but it requires monomers with a finely tuned balance of thermodynamic and kinetic parameters. We develop a multi-level screening pipeline that integrates DFT-calculated properties with a reaction-diffusion model to predict front behavior directly from the atomistic structure of the monomer. We experimentally validate a preliminary pipeline, identifying a new class of FROMP-capable furan-benzyne monomers, and uncover additional candidates from unexplored chemical spaces that overcome limitations of known systems.

Together, these studies demonstrate how high-throughput, mechanism-informed modeling can guide the discovery of molecules and materials that meet complex reactivity and performance criteria.