QCOF ML potentials tuned on COF data outperform general MACE models for defective systems and reveal higher thermal defect sensitivity in CTF-1 versus COF-LZU1 with nearly invariant low-strain mechanics.
& Jiang, D
2 Pith papers cite this work, alongside 473 external citations. Polarity classification is still indexing.
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A MACE-based machine learning potential enables large-scale simulation of defective COFs, revealing that defect impact on thermal transport depends on framework stiffness while mechanical strength is more vulnerable than stiffness.
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QCOF ML potentials tuned on COF data outperform general MACE models for defective systems and reveal higher thermal defect sensitivity in CTF-1 versus COF-LZU1 with nearly invariant low-strain mechanics.
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A MACE-based machine learning potential enables large-scale simulation of defective COFs, revealing that defect impact on thermal transport depends on framework stiffness while mechanical strength is more vulnerable than stiffness.