Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.
Using effect size—or why the P value is not enough
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Spectral analysis of four Bennu sites reveals statistically significant heterogeneity in hydration and silicate features at 2-10 m scales, with Nightingale encompassing the full observed range.
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A critical assessment of bonding descriptors for predicting materials properties
Quantum-chemical bonding descriptors improve machine learning predictions of materials properties and enable symbolic regression to recover intuitive expressions for force constants and thermal conductivity.