Distilled compact MLIPs from transfer-learned teachers reproduce observables more reliably than same-size models trained directly and enable practical PIMD umbrella sampling of water dissociation at TiO2 interface with NQE effects matching NMR.
https://arxiv.org/abs/2502.15582
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
VibroML automates remediation of dynamic instabilities in crystalline materials by combining MLIPs with genetic algorithms for polymorph search, finite-temperature MD validation, and compositional alloying to yield stable structures from databases like Alexandria.
Quantum zero-point effects contribute less to low-temperature dislocation glide in bcc metals than earlier empirical-potential studies indicated, leaving the simulation-experiment discrepancy unresolved.
Systematic tests show naive fine-tuning excels for single-task accuracy while multihead replay best preserves out-of-distribution robustness in MLIP adaptation.
Machine-learned force fields trained on coupled-cluster potential energy surfaces produce phonon dispersions and vibrational densities of states for solids that agree better with experiment than DFT-based models.
citing papers explorer
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Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry
Distilled compact MLIPs from transfer-learned teachers reproduce observables more reliably than same-size models trained directly and enable practical PIMD umbrella sampling of water dissociation at TiO2 interface with NQE effects matching NMR.
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VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials
VibroML automates remediation of dynamic instabilities in crystalline materials by combining MLIPs with genetic algorithms for polymorph search, finite-temperature MD validation, and compositional alloying to yield stable structures from databases like Alexandria.
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Revisiting quantum effects on dislocation glide in bcc metals from DFT calculations and machine-learning potentials
Quantum zero-point effects contribute less to low-temperature dislocation glide in bcc metals than earlier empirical-potential studies indicated, leaving the simulation-experiment discrepancy unresolved.
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Fine-tuning MLIP foundation models: strategies for accuracy and transferability
Systematic tests show naive fine-tuning excels for single-task accuracy while multihead replay best preserves out-of-distribution robustness in MLIP adaptation.
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Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
Machine-learned force fields trained on coupled-cluster potential energy surfaces produce phonon dispersions and vibrational densities of states for solids that agree better with experiment than DFT-based models.