Presents a path-integral molecular dynamics implementation of quantum annealing for global optimization of atomic structures using empirical or machine-learned potentials.
Magnus Rahm and Alexander J
4 Pith papers cite this work, alongside 406 external citations. Polarity classification is still indexing.
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An extended dual-solute segregation model with machine-learned pairwise energies predicts co-segregation bounds in Mg-based ternary alloys, validated by hybrid MD/MC and literature experiments.
Hybrid QM/ML forcefield framework couples DFT with MLIPs to enable scalable, chemically accurate simulations of solute-dislocation interactions, demonstrated on Sn/Fe segregation in Zr and magnetic effects in steel.
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Predicting co-segregation in alloys with solute-solute interactions
An extended dual-solute segregation model with machine-learned pairwise energies predicts co-segregation bounds in Mg-based ternary alloys, validated by hybrid MD/MC and literature experiments.