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Active subspace coarse-graining with spherical harmonics
Bandy, Leah Wojnar, Anna Pike, Logan Harrand, Quinn Pankavich, Stephen Pak, Alexander J.
Bandy, Leah
Wojnar, Anna
Pike, Logan
Harrand, Quinn
Pankavich, Stephen
Pak, Alexander J.
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2024-04
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Many groups of molecules exhibit self-assembly behavior to form large-scale hierarchical structures. Scientists are interested in identifying the molecular basis for self-assembly, but the spatial and temporal resolution of current experimental techniques precludes observation of assembly details at the nanoscale. Meanwhile, conventional all-atom computer simulations remain too costly, and coarse-grained simulations, which trade detailed information for lower computational complexity, remain difficult to apply to macromolecular assembly. We utilize a supervised dimension reduction approach called active subspaces to enable coarse-grained simulations of self-assembling systems at reduced computational cost (compared to all-atom) and increased accuracy (compared to other coarse-grained models). The active subspace method identifies the most important directions in an input parameter space that influence a corresponding output function. The goal of this project is to develop and formalize the active subspace framework to derive coarse-grained models from all-atom data for reversibly aggregating alanine peptides. Our strategy is to explore spherical harmonics as the input parameter space, corresponding to potential energies as the output. Preliminary results indicate that a reduced set of spherical harmonics can provide a descriptive basis useful for coarse-grained modeling and simulation.
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