Stable, Fast, and Accurate Kohn-Sham Matrix Reconstruction in Gaussian Basis for Open-Shell Molecular and...

Here we present a density matrix penalization method for finite basis Kohn-Sham (KS) matrix reconstruction in Gaussian basis representations. The method constructs a matrix-represented auxiliary KS Hamiltonian whose self-consistent density matrix and corresponding Gaussian-basis-representable real-space electron density reproduce a prescribed target, without assuming the recovery of a unique continuum local KS or exchange-correlation potential. This finite basis formulation is motivated by the numerical difficulties encountered by conventional inverse KS-DFT approaches, such as the Zhao-Morrison-Parr (ZMP) method, when a real-space penalty potential is projected onto a limited set of Gaussian basis matrix elements. Such a projection can strongly coarse-grain the constraining potential and lead to poorly constrained and inefficient self-consistent-field (SCF) optimizations. In the present method, the density matrix mismatch is defined in a Löwdin-orthogonalized basis, yielding a penalty functional that is invariant under basis rotations in that representation. The corresponding penalty Hamiltonian matrix contribution is derived analytically in the original nonorthogonal Gaussian basis. Across a wide range of penalty strengths, SCF optimization remains robust and efficient for various open-shell molecular and condensed-phase systems, while progressively tightening the penalty drives the density matrix and the associated real-space density into near machine precision agreement with the target for most systems. Benchmarks show that the method achieves substantially smaller attainable density deviations than conventional ZMP calculations in Gaussian bases. The method provides a stable, fast, and accurate route to finite basis KS matrix reconstruction and establishes a practical framework for density-matrix-based inverse reconstruction in Gaussian basis electronic structure calculations.