发表机构
NeuroTechNet S.A.S.; Universidad Nacional de Colombia（神经技术网公司; 哥伦比亚国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究基于收缩有向无环图，在消费级GPU上实现了双杂化激发态的解析导数非绝热耦合矩阵元等，将相关计算能力从数据中心硬件移植到普通桌面显卡，提升了激发能计算精度并完成了物理验证。
AI 中文摘要
非绝热动力学在每个核几何结构下都需要激发态梯度和态间非绝热耦合矩阵元（NACME），而双杂化泛函的精度此前无法用于该耦合计算。我们首次报道了双杂化激发态的解析导数NACME——该方法最初在hh-TDA方法中被延迟，此前仅Yu等人针对杂化泛函提供了相关推导——结合空穴-空穴和粒子-粒子塔姆-丹科夫（hhTDA/ppTDA）梯度与NACME，作为非对称原子轨道直接J/K核下闭合的单个收缩图的反向模式转置实现。其双杂化激发能将垂直激发平均绝对偏差从纯hhTDA的0.86 eV降至0.47 eV，消除了+0.53→+0.05 eV的过激发偏差，改善了10个态中的7个，同时过度校正了离子ππ*态——这是微扰双方法的预期失效，未被修剪。每个耦合项都与独立的“字面多电子波函数重叠”验证器（无共享代码路径）进行了~10^-4的验证，且在氨的n→σ*共价锥形交叉处具有物理意义，其中hhTDA/ppTDA流形恢复了F−2接缝，而绝热线性响应TDDFT按构造给出τ≡0。梯度、NACME和双杂化耦合均通过共享的乔列斯基分解J/K引擎在设备上运行，且通过原子轨道直接计算，可在消费级RTX 406（8 GB显存）上运行——该方法经配置文件引导，保留双精度位一致性，实现了~10^2倍的启动崩溃，将此前需要数据中心硬件的相关激发态导数能力置于普通桌面显卡上。
英文摘要
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 $ππ^*$ 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 $n\!\rightarrow\!σ^*$ \emph{covalent} conical intersection, where the \hhTDA/\ppTDA manifolds recover the $F\!-\!2$ seam and adiabatic linear-response TDDFT gives $τ\!\equiv\!0$ 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.
Comments17 pages, 11 figures
