Presents a path-integral molecular dynamics implementation of quantum annealing for global optimization of atomic structures using empirical or machine-learned potentials.
GPUMD: A package for constructing accurate machine -learned potentials and performing highly efficient atomistic simulations,
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cond-mat.mtrl-sci 3years
2026 3verdicts
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An extended dual-solute framework predicts co-segregation bounds in multicomponent alloys by machine-learning pairwise segregation energies that include solute-solute interactions and is validated on magnesium systems.
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 multicomponent alloys with solute-solute interactions
An extended dual-solute framework predicts co-segregation bounds in multicomponent alloys by machine-learning pairwise segregation energies that include solute-solute interactions and is validated on magnesium systems.