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Density-Functional Excited-State Gradients and Nonadiabatic Couplings on a Consumer GPU from a Contraction-DAG

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Physics > Chemical Physics

arXiv:2608.06536 (physics)
[Submitted on 6 Aug 2026]

Title:Density-Functional Excited-State Gradients and Nonadiabatic Couplings on a Consumer GPU from a Contraction-DAG

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Abstract:Nonadiabatic dynamics needs an excited-state gradient and an interstate nonadiabatic coupling matrix element (NACME) at every nuclear geometry, and a double-hybrid functional's accuracy has been unavailable for the coupling. We report the first analytic derivative NACME for a double-hybrid excited state---deferred in the original hh-TDA method and supplied for hybrids only by Yu \emph{et al.}---derived, with the hole-hole and particle-particle Tamm--Dancoff (\hhTDA/\ppTDA) gradients and NACMEs, as a single reverse-mode transpose of one contraction graph closed under a non-symmetric atomic-orbital-direct $J/K$ kernel. Its double-hybrid excitation energy lowers the vertical-excitation mean absolute deviation from bare-\hhTDA\ $0.86$ to $0.47$~eV and removes the $+0.53\!\rightarrow\!+0.05$~eV over-excitation bias, improving seven of ten states while over-correcting the ionic $\pi\pi^*$ states---the expected perturbative-doubles failure, reported not trimmed. Every coupling is validated to $\sim\!10^{-4}$ against an independent \emph{literal many-electron wavefunction-overlap} oracle that shares no code path with the method, and is physically meaningful at the ammonia $n\!\rightarrow\!\sigma^*$ \emph{covalent} conical intersection, where the \hhTDA/\ppTDA manifolds recover the $F\!-\!2$ seam and adiabatic linear-response TDDFT gives $\tau\!\equiv\!0$ by construction. Gradients, NACMEs, and the double-hybrid coupling all run device-resident and AO-direct through one shared Cholesky-decomposed $J/K$ engine within the 8\,GB of a consumer RTX~4060 (a profile-guided $\sim\!10^2\times$ launch collapse preserving double-precision bit-identity)---placing on a commodity desktop card a correlated excited-state derivative capability that has until now required datacenter hardware.
Comments: 17 pages, 11 figures
Subjects: Chemical Physics (physics.chem-ph); Hardware Architecture (cs.AR); Machine Learning (cs.LG)
MSC classes: 81-08, 81V55, 65F15, 68W10
ACM classes: G.1.0; G.4; J.2
Cite as: arXiv:2608.06536 [physics.chem-ph]
  (or arXiv:2608.06536v1 [physics.chem-ph] for this version)
  https://doi.org/10.48550/arXiv.2608.06536
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Rubén Darío Guerrero Mr. [view email]
[v1] Thu, 6 Aug 2026 19:40:08 UTC (5,491 KB)
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