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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Surveys theoretical concepts of polariton Bose-Einstein condensates, emphasizing that linear and non-interacting effects explain much of the phenomenology including bosonic correlations and coherence buildup.
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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.
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Polariton BECs: Theory and Concepts
Surveys theoretical concepts of polariton Bose-Einstein condensates, emphasizing that linear and non-interacting effects explain much of the phenomenology including bosonic correlations and coherence buildup.