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.
Fan et al., General-purpose machine-learned potential for 16 elemental metals and their alloys, Dec
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A Hybrid Quantum Mechanics Machine Learning Forcefield (QM/ML) Framework for Accurate Solute-Dislocation Interaction Simulations
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.