most citedData-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning

1 citations · 1 across the 2 of their papers we have counts for

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2 papers

cond-mat.mtrl-sci2024★ 1 cited

Data-Driven Modeling of Dislocation Mobility from Atomistics using Physics-Informed Machine Learning

Yifeng Tian, Soumendu Bagchi, Liam Myhill +5

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic def…

physics.app-ph2024

Strain Functionals: A Complete and Symmetry-adapted Set of Descriptors to Characterize Atomistic Configurations

Edward M. Kober, Jacob P. Tavenner, Colin M. Adams +1

Extracting relevant information from atomistic simulations relies on a complete and accurate characterization of atomistic configurations. We present a framework for characterizing…