A differentiable pipeline uses continuous atom occupancy and gradient descent plus a neural network to optimize short-range order in multi-element alloys directly for target stiffness properties.
Strengthening in multi-principal element alloys with local-chemical-order roughened dislocation pathways,
3 Pith papers cite this work. Polarity classification is still indexing.
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cond-mat.mtrl-sci 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Anharmonic calculations show random CoCrNi ISFE decreases and stays negative with temperature while LCO ISFE stays positive from 0-1000 K, with MD confirming unbounded vs finite dislocation dissociation.
ML model using ideal entropy plus simulation features (energy above hull, heat capacity change, icosahedral fraction) predicts metallic glass critical cooling rates with R²=0.78 in leave-one-chemical-system-out cross-validation on 34 alloys.
citing papers explorer
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Differentiable inverse design of short-range order in high-entropy alloys: from target sro to target property
A differentiable pipeline uses continuous atom occupancy and gradient descent plus a neural network to optimize short-range order in multi-element alloys directly for target stiffness properties.
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Finite Temperature Stacking Fault Stability in Random and Locally Ordered CoCrNi beyond the Harmonic Approximation
Anharmonic calculations show random CoCrNi ISFE decreases and stays negative with temperature while LCO ISFE stays positive from 0-1000 K, with MD confirming unbounded vs finite dislocation dissociation.
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Machine learning metallic glass critical cooling rates through elemental and molecular simulation based featurization
ML model using ideal entropy plus simulation features (energy above hull, heat capacity change, icosahedral fraction) predicts metallic glass critical cooling rates with R²=0.78 in leave-one-chemical-system-out cross-validation on 34 alloys.