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Distilling Large-Scale Machine Learning Interatomic Potentials to Predict Long-Timescale Electrolyte Transport Properties

Amin, Ishan et al.
arXiv (All) · Papers
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chemical-physicscomputational-physicsmachine-learningmaterials-sciencesoft-condensed-matter
Accurately predicting the transport properties of liquid electrolytes is critical for next-generation battery design and requires force fields that are both quantum-mechanically accurate and fast enough for long-timescale simulation. Recent large-scale pretrained machine learning interatomic potenti
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