Physics-constrained equivariant GNN warm starts cut DFT+DMFT self-consistency iterations by 2-4x across Fe, FeO and NiO, enabling an MLIP-based NVE coexistence simulation that yields a 6225 K melting temperature for hcp-Fe at 330 GPa.
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Neural networks trained with 10-100x fewer examples than prior work approximate CT-QMC impurity solvers in DMFT, delivering comparable accuracy on interpolation and accelerating simulations up to 5x when used as initial guesses for lower temperatures.
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Physics-Constrained Self-Energy Warm Starts for Charge-Self-Consistent DFT+DMFT: Application to Iron at Core Conditions
Physics-constrained equivariant GNN warm starts cut DFT+DMFT self-consistency iterations by 2-4x across Fe, FeO and NiO, enabling an MLIP-based NVE coexistence simulation that yields a 6225 K melting temperature for hcp-Fe at 330 GPa.
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Neural networks as low-cost surrogates for impurity solvers in quantum embedding methods
Neural networks trained with 10-100x fewer examples than prior work approximate CT-QMC impurity solvers in DMFT, delivering comparable accuracy on interpolation and accelerating simulations up to 5x when used as initial guesses for lower temperatures.