26th Australian Institute of Physics Congress and the Australian and New Zealand Conference on Optics and Photonics

From Quantum Molecular Data to Predictive Plasma Models for Electronegative Dielectric Gases

Not scheduled

20m

Belinda Hutchinson Building (The University of Sydney )

Contributed Oral AIP | Atomic and Molecular Physics (ATMOP)

Description

Predictive plasma modelling depends critically on the quality of the underlying atomic and molecular physics data: scattering cross sections, attachment and ionisation thresholds, dissociation energetics, fragment stabilities, and reaction pathways. This is especially important for electronegative dielectric gases, where small errors in molecular energetics can propagate into large uncertainties in plasma composition, swarm transport, insulation strength, and post-arc recovery behaviour.

This presentation will describe recent progress toward a more systematic first-principles data pipeline for incorporating state-of-the-art molecular physics into plasma models. The work is motivated by sulphur hexafluoride, SF₆, a technologically important but high-global-warming-potential dielectric gas, and by the need to assess lower-impact replacement candidates. Recent quantum chemical calculations are being used to examine electron attachment, fragmentation energetics, and thermochemical consistency across SF₆ and related dielectric molecules, providing more physically constrained plasma-chemical models as a foundation for computational search of potential SF₆ replacements.

A second strand of the work reviews the emerging role of quantum algorithms in future scattering calculations. While fault-tolerant quantum computing is not yet a practical replacement for established close-coupling, R-matrix, or many-body scattering methods, recent algorithmic developments suggest possible long-term routes for treating strongly correlated molecular targets, resonance structure, and high-dimensional electronic dynamics. The immediate focus of this review is to identify where quantum algorithms may eventually reduce bottlenecks in generating reliable electron-molecule collision data, and where classical methods remain the appropriate benchmark.

Together, these activities form part of a broader programme aimed at improving the traceability, physical consistency, and predictive value of plasma modelling for energy, environmental, and industrial technologies.