A Fourier neural operator that maps chemical potential fields to polymer densities can be trained per species and composed to accelerate field-theoretic simulation of six-component mixtures, with speedups growing with polymer length.
Coacervation drives morphological diversity of mRNA encapsulating nanoparticles
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abstract
The spatial arrangement of components within an mRNA encapsulating nanoparticle has consequences for its thermal stability, which is a key parameter for therapeutic utility. The mesostructure of mRNA nanoparticles formed with cationic polymers have several distinct putative structures: here, we develop a field theoretic simulation model to compute the phase diagram for amphiphilic block copolymers that balance coacervation and hydrophobicity as driving forces for assembly. We predict several distinct morphologies for the mesostructure of these nanoparticles, depending on salt conditions and hydrophobicity. We compare our predictions with cryogenic-electron microscopy images of mRNA encapsulated by charge altering releasable transporters. In addition, we provide a GPU-accelerated, open-source codebase for general purpose field theoretic simulations, which we anticipate will be a useful tool for the community.
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Scaling field-theoretic simulation for multi-component mixtures with neural operators
A Fourier neural operator that maps chemical potential fields to polymer densities can be trained per species and composed to accelerate field-theoretic simulation of six-component mixtures, with speedups growing with polymer length.