Optimizing ring-polymer normal-mode frequencies to exact harmonic radii of gyration produces Eco path integrals that converge like fourth-order Suzuki-Chin methods at pure Trotter cost.
Magnus Rahm and Alexander J
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Presents a path-integral molecular dynamics implementation of quantum annealing for global optimization of atomic structures using empirical or machine-learned potentials.
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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Economised path integrals
Optimizing ring-polymer normal-mode frequencies to exact harmonic radii of gyration produces Eco path integrals that converge like fourth-order Suzuki-Chin methods at pure Trotter cost.
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Quantum annealing for materials
Presents a path-integral molecular dynamics implementation of quantum annealing for global optimization of atomic structures using empirical or machine-learned potentials.
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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.
- Predicting co-segregation in alloys with solute-solute interactions