Hybrid simulation and non-Euclidean elasticity theory demonstrate that clathrin coats develop adaptive rigidity and memory during growth, producing flat, stalled, or closed outcomes through two energy-landscape gates and matching experiments without fitted parameters.
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A runtime-trained, uncertainty-driven ML model accelerates kinetic Monte Carlo simulations of atomistic thin-film growth while retaining fidelity to interatomic potentials.
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A Self-Evolving Machine-Learning-Based Kinetic Monte Carlo Method for Modelling Thin-Film Growth
A runtime-trained, uncertainty-driven ML model accelerates kinetic Monte Carlo simulations of atomistic thin-film growth while retaining fidelity to interatomic potentials.