COGITO creates accurate tight-binding models from DFT that match MLWF accuracy while keeping the orbitals chemically interpretable and projected onto atomic centers.
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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.
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Crystal Orbital Guided Iteration to Atomic Orbitals: A Pathway to Chemically Adaptive Atomic Orbitals from DFT
COGITO creates accurate tight-binding models from DFT that match MLWF accuracy while keeping the orbitals chemically interpretable and projected onto atomic centers.
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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.