Multi-fidelity fine-tuning of a DFT-trained MACE MLIP with limited QMC energies yields near-QMC accuracy for S-vacancy energetics and migration in MoS2.
Frenkel and Smit
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Upscaling DFT-trained machine-learning interatomic potential toward Quantum Monte Carlo accuracy: Sulfur-vacancy migration in monolayer MoS$_2$ as a testbed
Multi-fidelity fine-tuning of a DFT-trained MACE MLIP with limited QMC energies yields near-QMC accuracy for S-vacancy energetics and migration in MoS2.