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
Machine learning elastic constants of multi-component alloys
2 Pith papers cite this work. Polarity classification is still indexing.
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A hybrid estimation framework combines simplified reference dynamics with a data-driven surrogate sensor model to reconstruct system states from incomplete measurements.
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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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State Forecasting in an Estimation Framework with Surrogate Sensor Modeling
A hybrid estimation framework combines simplified reference dynamics with a data-driven surrogate sensor model to reconstruct system states from incomplete measurements.