Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2201.13205

Vibronic mass computation for the $EF$-$GK$-$H\bar{H}$ $^1Σ_\text{g}^+$ manifold of molecular hydrogen

Edit Mátyus·Dávid Ferenc
View original source ↗Read source document ↗Export metadata ↓

Abstract

A variational procedure is described for the computation of the non-adiabatic mass-correction tensor applicable for multi-dimensional electronic manifolds. The 30-year-old computations of Wolniewicz, Dressler, and their co-workers are appended with the computed vibronic mass-correction functions corresponding to the $EF$-$GK$-$H\bar{H}$-$S5$-$S6$ $^1Σ_\text{g}^+$ manifold of the hydrogen molecule. Initial results are reported for the vibronic energies. Necessary further improvements and further developments are discussed.

Explore related subjects

physics.chem-phquant-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Edit Mátyus, Dávid Ferenc. 2022-03-17. Vibronic mass computation for the $EF$-$GK$-$H\bar{H}$ $^1Σ_\text{g}^+$ manifold of molecular hydrogen. https://doi.org/10.1080/00268976.2022.2074905

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Accelerating ab initio spin-phonon relaxation simulation of single-ion magnets by quantum embedding and spatial truncation

Single-ion magnets (SIMs) show promise for high-density storage and quantum computing, but predicting spin-phonon coupling (SPC) and magnetic relaxation remains challenging due to the need for numerous non-equilibrium multiconfigurational calculations. Recent advances in quantum embedding methods offer a potential route to address this issue. In this work, density matrix embedding theory (DMET) combined with complete active space self-consistent field (CASSCF) is benchmarked for the static magnetic properties and spin-phonon coupling (SPC) parameters of Dy$^{3+}$-based SIMs. The method is further combined with spatial truncation to calculate SPC parameters for these SIMs. It is found that truncating the space near the first coordination sphere reduces the computational cost dramatically while keeping the errors in the effective energy barrier and relaxation time-scale negligible. This study provides a practical calculation framework for accurate and efficient spin dynamics prediction, laying the foundation for the rational design of high-performance single-molecule magnets.

physics.chem-ph↗
arXiv2026

From Transition-State Geometry to Gap Times: What Lagrangian Betweenness Measures in Chemical Reaction Dynamics

A chemical transition state is a phase-space bottleneck: trajectories approach along stable directions and leave along unstable directions. Lagrangian betweenness (LB), introduced in finite-time transport theory for fluid flows, combines backward and forward deformation in a way suggestive of this gather-and-disperse geometry. We ask what LB measures when the transition-state geometry is known. For a linear rank-one saddle, LB is spatially constant even though the stable and unstable manifolds are present. Relative nonlinear corrections remain small on shrinking initial neighborhoods, including observation times on the logarithmic local escape scale. At fixed spatial resolution, however, increasing observation time can concentrate the spatial variation of LB near a stable or unstable manifold. A separable quartic Hamiltonian makes these two limits explicit. A nonseparable Hamiltonian shows local flattening near a hyperbolic periodic orbit. We then use HCN/CNH isomerization, where dividing surfaces and gap times are established by phase-space transition-state theory. Before any exit, larger LB generally accompanies longer eventual gap times. The incoming reactive action provides a dynamical interpretation: initial conditions closer to the normally hyperbolic invariant manifold have longer local passages and greater accumulated stretching during passage. Among trajectories still inside at 0.5 ps that exit before 5 ps, early LB does not usefully rank exit times. LB therefore characterizes transition-state-organized transport in this example; a large value is neither an intrinsic manifold marker nor a general measure of molecular residence time.

physics.chem-ph↗
arXiv2026

Automated AFGL quantum number assignment for CO$_2$ isotopologues using a graph neural network

Accurate quantum number assignment for calculated molecular energy levels is a critical bottleneck in generating line broadening parameters for comprehensive line lists for radiative transfer applications. We present an automated pipeline for assigning Air Force Geophysics Laboratory (AFGL) quantum numbers to CO2 calculated rovibrational states lying below 15,000cm$^{-1}$ across all 12 stable isotopologues. A GraphSAGE graph neural network is trained transductively on empirical (MARVEL) energy levels, exploiting inter-isotopologue perturbation chains and intra-isotopologue rotational ladder edges to propagate assignment information to unlabelled calculated states. Physical uniqueness is enforced locally by a Hungarian algorithm solver operating within groups of states sharing the same polyad, rotational quantum number, and parity. A five-generation bootstrap loop iteratively promotes high-confidence predictions into the training set, expanding coverage without additional labelling effort. The pipeline assigns 224,650 previously unlabelled states over 12 isotopologues, accounting for 10.7$\%$ of all available states (including MARVEL-derived levels), with coverage now increased to 97.4$\%$ below 5000cm$^{-1}$. The pipeline includes a novel decision tree method for converting AFGL to Herzberg notation in asymmetric isotopologues, while the architecture and Hungarian uniqueness enforcement are applicable beyond CO2, any molecular system with a conserved polyad-like quantum number and a large body of unlabelled computed states is a natural target for this approach, suggesting a pathway toward automated quantum number annotation for the next generation of large-scale computed line lists.

physics.chem-ph↗