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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Spatial statistics on voxelized structures using FFT correlations and PCA yield low-dimensional convex features that support accurate predictions with as few as 10 training samples.
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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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Spatial statistics for screening molecular structures
Spatial statistics on voxelized structures using FFT correlations and PCA yield low-dimensional convex features that support accurate predictions with as few as 10 training samples.