Restoring the Conical Intersection Topology using Convex Density Functional Theory

TL;DR

This paper introduces Convex DFT (CVX-DFT), a novel approach that ensures smooth, continuous solutions at conical intersections, improving the reliability of non-adiabatic molecular simulations.

Contribution

The authors develop CVX-DFT, a convexity-enforcing framework that guarantees unique, continuous electronic solutions across degeneracies, addressing a key limitation of traditional DFT methods.

Findings

CVX-DFT produces smooth intersection seams comparable to multireference methods.

The method guarantees a unique, continuous solution at degeneracies.

CVX-DFT is computationally efficient and robust for non-adiabatic simulations.

Abstract

Conical intersections are central to the description of photophysics and photochemistry. Nevertheless, in non-adiabatic molecular dynamics simulations, they are fundamentally challenging for single-reference electronic structure methods. Density functional theory (DFT) and its time-dependent extension (TDDFT) represent the most widely used theoretical approaches in physics, chemistry, and biology. However, the treatment of ground and excited states as separate problems leads to breakdowns in the topological structure of potential energy surfaces near conical intersections. In this work, we solve this long-standing issue by presenting Convex DFT (CVX-DFT), a framework that, by explicitly enforcing convexity of the variational problem within an appropriately defined subspace, guarantees a unique and continuous electronic solution across regions of degeneracies. We demonstrate that CVX-DFT yields smooth and physically meaningful intersection seams by comparison with reference methods, such as multireference wave function methods. In this way, we establish the method as a robust and computationally efficient DFT approach for treating electronically degenerate regions. These developments represent a critical step toward reliable non-adiabatic simulations beyond the limitations of conventional TDDFT.