High-Throughput Computational Framework for the Discovery of Structurally Diverse Monomers in High-Strength Aramid Fibers

High-performance
aramid fibers are valued for exceptional strength
and thermal stability, yet further improvement requires discovery
of new monomer chemistries. We present a high-throughput computational
framework that integrates cheminformatics filtering, molecular dynamics
(MD) simulations, and an automated simulation interface to accelerate
aramid monomer design. From 93,972 dicarboxylic acids retrieved from
PubChem, sequential structural filters yielded 3,385 synthetically
feasible candidates. These were systematically evaluated through MD
simulations of interchain (polymer–polymer) and chain–solvent
interaction energies, using Technora as a benchmark. The screening
identified 149 monomers with stronger interchain cohesion and improved
solubility in N-methyl-2-pyrrolidone (NMP). Reactive
MD tensile simulations further highlighted 44 monomers predicted to
achieve tensile strengths exceeding 26 GPasignificantly outperforming
conventional aramids and increasing the discovery “hit rate”
from ∼9% (random) to ∼29% (targeted). Topological and
functional group analyses revealed that these monomers span diverse
chemical space and commonly feature fused aromatics, heterocyclic
cores, and rigid bridging units, with strong hydrogen bonding and
π–π stacking as dominant contributors to enhanced
intermolecular cohesion. Although demonstrated with NMP solvent and
selected aramid polymers, the framework is broadly applicable to other
solvents and polymer classes, particularly amide-containing systems,
establishing a versatile platform for high-throughput polymer discovery.