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Self-consistent Hessian-level meta-generalized gradient approximation

Chemical Physics 2026-04-09 v1 Materials Science

Abstract

The ϑ\vartheta-MGGA class of density functionals is formally reformulated as Hessian-level meta-generalized gradient approximations (HL-MGGAs). In contrast to standard meta-GGAs that rely on the orbital-dependent kinetic-energy density or the density Laplacian, HL-MGGAs utilize the full density Hessian. We introduce a simplified, non-empirical functional, ϑ\vartheta-PBE, and present a roadmap for its self-consistent implementation within the projector augmented-wave (PAW) method. By utilizing the complete set of spatial second-order density derivatives, the functional's underlying descriptor successfully distinguishes between distinct one-electron density limits, such as single-center atomic densities and two-center bonds, that standard iso-orbital indicators often conflate. Benchmarks across molecular and solid-state datasets reveal that while ϑ\vartheta-PBE delivers accurate chemisorption energies and molecular properties, challenges remain in predicting bulk lattice constants. Ultimately, this work demonstrates the physical utility and feasibility of designing orbital-independent, Hessian-based exchange-correlation functionals.

Keywords

Cite

@article{arxiv.2604.07046,
  title  = {Self-consistent Hessian-level meta-generalized gradient approximation},
  author = {Pooria Dabbaghi and Juan Maria García Lastra and Piotr de Silva},
  journal= {arXiv preprint arXiv:2604.07046},
  year   = {2026}
}

Comments

35 pages, 5 figures