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

초록

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 similar to 9% (random) to similar 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 pi-pi 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.

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