Jun Ding

activity

20152023

most citedTunable stacking fault energies by tailoring local chemical order in CrCoNi medium-entropy alloys

866 citations · 1k across the 7 of their papers we have counts for

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Showing 2020 · cond-mat.mtrl-sciShow all

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cond-mat.mtrl-sci2020

Predicting the propensity for thermally activated $β$ events in metallic glasses via interpretable machine learning

Qi Wang, Jun Ding, Evan Ma

The elementary excitations in metallic glasses (MGs), i.e., $β$ processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys.…

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Predicting the propensity for thermally activated $β$ events in metallic glasses via interpretable machine learning