STOKES-DOT/GradTDDFT: GradTDDFT v1.1.0
GradTDDFT v1.1.0 GradTDDFT v1.1.0 provides a JAX-based framework for differentiable DFT, TDA/TDDFT, and neural exchange-correlation functional training. Highlights PySCF-aligned numerical grids: The standalone JAX grid now matches PySCF-style Lebedev quadrature, Treutler–Ahlrichs radial grids, NWChem pruning, and Becke partitioning for grid levels 0–9. Improved conventional DFT/TDDFT: Optimized restricted and unrestricted DFT, TDA, and full-TDDFT calculations for non-neural XC functionals, including matrix-free Davidson solvers and JAX GMRES for implicit SCF differentiation. Implicit excited-state differentiation: Supports implicit differentiation of TDA/TDDFT eigenvalues and eigenvectors without backpropagating through the full eigensolver history. Roadmap PySCFAD-based numerical integration — under testing Automatic-differentiation nonadiabatic coupling vectors Equivariant graph-neural-network atomic-orbital basis functions — under testing Dressed TDDFT beyond adiabatic linear-response TDDFT Full Changelog: https://github.com/STOKES-DOT/GradTDDFT/compare/v1.0.0...v1.1.0