A physics-informed autoencoder compresses 3D charge density into a 16x16x16x16 latent representation that, combined with MAGPIE descriptors, predicts bulk modulus, Young's modulus, shear modulus, formation energy, and Debye temperature with R2 values of 0.94, 0.88, 0.87, 0.96, and 0.89 on 6059 DFT-s
Charting the complete elastic properties of inorganic crystalline compounds
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Averaged covalent and ionic bond strengths correlate with and can estimate oxygen vacancy migration barriers across rutile 3d transition-metal dioxides after fitting two parameters to DFT data.
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.
CrabNet outperforms MODNet and random forest models when predicting battery electrode properties from composition, with cross-validation and clustering confirming coherent groupings.
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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.