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
and Toher, Cormac and Curtarolo, Stefano and Ceder, Gerbrand and Persson, Kristin A
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cond-mat.mtrl-sci 2years
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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.
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Physics Aware Representation Learning on Electronic Charge Density for Materials Property Prediction
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
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Bond-Strength-Based Understanding of Oxygen Vacancy Migration Barriers in Rutile Oxides
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.