An adaptive fine-tuning workflow for foundation-model MLIPs enables efficient CSP in the CaFeNi ternary, reproducing the low-pressure hull and predicting a new phase Ca6FeNi stable above 100 GPa.
The Journal of Chemical Physics 145(17), 170901 (2016)
4 Pith papers cite this work. Polarity classification is still indexing.
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DeepPolaron ML-MD simulations show rutile electrons form Ti-localized polarons hopping along [001] with 39 meV barrier and 4.4e-2 cm2/Vs mobility, while anatase holes form O-localized polarons hopping to second neighbors with 139 meV barrier and 1.4e-3 cm2/Vs mobility.
Exact force fields are variationally induced from DFT by pulling back the energy functional, density, and response function from external potential space to nuclear positions.
Neural network approximates potential from Hamiltonian trajectories then equation discovery extracts algebraic expression matching ground truth on oscillators, central force, and Coulomb problems.
citing papers explorer
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Adaptive fine-tuning of foundation models for crystal structure prediction: Discovery of high-pressure phases in the CaFeNi system
An adaptive fine-tuning workflow for foundation-model MLIPs enables efficient CSP in the CaFeNi ternary, reproducing the low-pressure hull and predicting a new phase Ca6FeNi stable above 100 GPa.
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Polaron Transport in TiO$_{2}$ from Machine Learning Molecular Dynamics
DeepPolaron ML-MD simulations show rutile electrons form Ti-localized polarons hopping along [001] with 39 meV barrier and 4.4e-2 cm2/Vs mobility, while anatase holes form O-localized polarons hopping to second neighbors with 139 meV barrier and 1.4e-3 cm2/Vs mobility.