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LCO-sensitive graph neural network framework for high-accuracy energy mapping in NiCoCr medium-entropy alloys.
(Published in a Q2 Journal : Refer to the Journal Publication Page)
Abstract:
This study presents a pathway of A Graph Convolutional Neural Network (GCNN) accurately mapping atomic configurations to potential energies in NiCoCr medium-entropy alloys using hybrid MC/MD simulation, capturing subtle energy variations associated with local chemical order (LCO).Â
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Effect of Grain Size and Local Chemical Order on Creep Resistance in MoNbTaW Refractory High-Entropy Alloy: A Molecular Dynamics Study.
(Published in a Q2 Journal : Refer to the Journal Publication Page)
Abstract:
This study used high-fidelity atomistic simulations with a machine-learning interatomic potential to examine creep deformation in MoNbTaW refractory high-entropy alloys under varying stress and temperature conditions. The results showed that larger grain size and the introduction of local chemical order both improve creep resistance by reducing grain-boundary-driven deformation and strengthening the microstructure for extreme-environment applications.
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