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This paper introduces the Real-space Atomic Decomposition NETwork (radnet), a deep learning framework designed to accurately predict polarization, electronic dielectric permittivity tensors, and related quantities in solids. The authors demonstrate its effectiveness through examples like GaAs and BN, achieving high accuracy in predicting Raman spectra and calculating Born-effective charges and LO-TO splitting. The study highlights the advantages of using deep learning for modeling complex solid-state proper

Author

Elie R. Haddad

Language

EN