Machine learning on dislocation loop energies reveals a single universal irregularity parameter that controls both thermodynamic properties and self-climb dynamics of non-compact prismatic loops.
Panels (b-1)–(b-3) show data generated using similar comparisons for the SIA-containing strings computed forn cut = 5, 10, and 15, respectively
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Thermodynamics and dynamics of non-compact prismatic dislocation loops simulated using a machine-learning model
Machine learning on dislocation loop energies reveals a single universal irregularity parameter that controls both thermodynamic properties and self-climb dynamics of non-compact prismatic loops.