A unified variational framework for the inverse Kohn-Sham problem

TL;DR

This paper introduces a unified variational framework for the inverse Kohn-Sham problem, clarifying the relationships among existing methods and providing a comprehensive optimization perspective.

Contribution

It identifies the fixed-density constrained search as the core variational principle and classifies various inversion methods within a unified optimization-theoretic framework.

Findings

Reveals the inverse KS problem as a dual variational problem.

Classifies existing inversion methods as different formulations within the framework.

Clarifies roles of ambiguity, normalization, and stability in inversion techniques.

Abstract

The inverse Kohn-Sham (KS) problem seeks a local effective potential whose noninteracting ground state reproduces a prescribed electron density. Existing inversion formulations are often expressed in disparate languages, including reduced variational optimization, penalty regularization, response-based iteration, and PDE-constrained optimization. In this work, we develop a unified variational framework for inverse KS theory in two steps. First, we identify the fixed-density noninteracting constrained search embedded in exact density functional theory as the natural variational anchor of inverse KS inversion. In this setting, the KS potential appears as the variational dual object associated with density reproduction. Second, we show how the principal inversion formulations may be understood as realizations of the same inverse-KS structure and how they fit into a broader optimization-theoretic classification according to whether the KS state equations and density-reproduction condition are treated as objectives, constraints, penalties, or feasibility relations. Within this framework, Wu-Yang appears as a reduced exact-multiplier formulation, Zhao-Morrison-Parr as a quadratic-penalty relaxation, and PDE-constrained approaches as explicit state-constraint formulations. The same viewpoint also accommodates augmented-Lagrangian and all-at-once residual formulations, and clarifies the roles of additive-constant ambiguity, asymptotic normalization, nonsmooth variational structure, and weak-gap instability across inversion methods.