arXiv subjects

Marco G. Barnfield

Publications and source records attributed to Marco G. Barnfield.

3 recordsLinked to original sources

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↗

arXiv2026

Machine learning isotope shifts in molecular energy levels

Recent advances in the use of High-Resolution Cross-Correlation Spectroscopy (HRCCS) to detect molecular species in exoplanet atmospheres, presents a new challenge for the accuracy of reference spectroscopic line lists. While parent isotopologues of key atmospheric tracers are often well-characterized, minor isotopologues, crucial for diagnosing planetary formation histories and evolution, suffer from a scarcity of experimental data, often leading to reliance on less accurate theoretical predictions. In this work, a comprehensive machine learning framework is designed to mitigate these inaccuracies by modelling the residual errors of the isotopologue extrapolation (IE) method used within the ExoMol project. A fully connected neural network architecture for carbon dioxide (CO$_2$) is shown to predict energy corrections with high fidelity, reducing the mean absolute error (MAE) relative to the original IE approach for more than 87\% of the levels when benchmarked against empirical (\Marvel) energies. Furthermore, development of a novel hybrid, molecule-aware transfer learning architecture is presented that successfully propagates correction patterns from the data-rich CO$_2$ system to the data-poor carbon monoxide (CO) system. This transfer learning approach yields MAE improvements in over 93\% of CO samples, demonstrating that physical correction factors related to isotopic substitution can be generalized across chemically related molecular systems. Updated and improved line lists are presented for 11 CO$_2$ isotopologues and energy levels for excited states of CO isotopologues are predicted. The methodology establishes a scalable, data-driven paradigm for refining molecular line lists, helping to bridge the gap between theoretical calculations and experimental precision.

astro-ph.EP↗

arXiv2026

ExoMol line lists -- LXIII: ExoMol line lists for 12 isotopologues of CO$_2$

Extensive rovibrational line lists are constructed for 12 isotopologues of carbon dioxide: $^{12}$C$^{16}$O$_2$, $^{13}$C$^{16}$O$_2$, $^{12}$C$^{17}$O$_2$, $^{13}$C$^{17}$O$_2$, $^{12}$C$^{18}$O$_2$, $^{13}$C$^{18}$O$_2$, $^{16}$O$^{12}$C$^{17}$O, $^{16}$O$^{12}$C$^{18}$O, $^{16}$O$^{13}$C$^{17}$O, $^{16}$O$^{13}$C$^{18}$O, $^{17}$O$^{12}$C$^{18}$O, and $^{17}$O$^{13}$C$^{18}$O. The variational program TROVE was employed together with an exact kinetic energy operator, accurate empirical potential energy surface (Ames-2) and the ab initio dipole moment surface Ames-2021-40K. Empirical energy levels from the most recent MARVEL analyses, as well as from the HITRAN and CDSD databases, are used to replace calculated values where available. The line lists are further supplemented by assigning AFGL quantum numbers using machine-learning based estimators. The resulting data were employed to generate opacities with four radiative transfer codes, TauREx, ARCiS, NEMESIS, and petitRADTRANS, both for individual isotopologues and for CO$_2$ at terrestrial isotopic natural abundance. All line lists and associated data are available at www.exomol.com.

astro-ph.EP↗