Machine Learning for Chemistry Reduction in N$_2$-H$_2$ Low-Temperature Plasmas

Diogo R. Ferreira; Alexandre Lança; Luís Lemos Alves

Home/Archive/arXiv

arXiv · 2409.05914

Machine Learning for Chemistry Reduction in N$_2$-H$_2$ Low-Temperature Plasmas

Diogo R. Ferreira·Alexandre Lança·Luís Lemos Alves

View original source ↗Read source document ↗Export metadata ↓

Abstract

Low-temperature plasmas are partially ionized gases, where ions and neutrals coexist in a highly reactive environment. This creates a rich chemistry, which is often difficult to understand in its full complexity. In this work, we develop a machine learning model to identify the most important reactions in a given chemical scheme. The training data are an initial distribution of species and a final distribution of species, which can be obtained from either experiments or simulations. The model is trained to provide a set of reaction weights, which become the basis for reducing the chemical scheme. The approach is applied to N$_2$-H$_2$ plasmas, created by an electric discharge at low pressure, where the main goal is to produce NH$_3$. The interplay of multiple species, as well as of volume and surface reactions, make this chemistry especially challenging to understand. Reducing the chemical scheme via the proposed model helps identify the main chemical pathways.

Explore related subjects

physics.plasm-ph

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Diogo R. Ferreira, Alexandre Lança, Luís Lemos Alves. 2024-09-12. Machine Learning for Chemistry Reduction in N$_2$-H$_2$ Low-Temperature Plasmas. https://arxiv.org/abs/2409.05914

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

KEEP EXPLORING

Related papers

arXiv2026

Runaway electron interactions with whistler waves in tokamak plasmas: energy-dependent transport scaling

Resonant interactions between high energy runaway electrons (REs) and whistler waves are a promising mechanism for RE mitigation in tokamak plasmas. While prior studies have largely relied on quasi-linear diffusion models in simplified geometries, we present a first-principles-informed framework that models RE-whistler interactions in a 3D tokamak equilibrium. This is achieved by coupling AORSA, which computes whistler eigenmodes for a given tokamak plasma equilibrium, and KORC, a kinetic orbit code that tracks full orbit RE trajectories in prescribed wave fields. Our results demonstrate that REs undergo scattering to large pitch angles and exhibit anomalous diffusion in both pitch-angle and kinetic energy space. Crucially, we observe a transition between diffusive, sub-diffusive, and super-diffusive transport regimes as a function of initial RE energy - an effect not captured by existing quasi-linear models. This anomalous transport behavior represents a significant advancement in understanding RE dynamics in the presence of wave - particle interactions. By identifying the conditions under which anomalous diffusion arises, this work lays the theoretical foundation for designing targeted, wave-based mitigation strategies in future tokamak experiments.

physics.plasm-ph↗

arXiv2026

Geodesic Acoustic Modes in pair plasmas confined in tokamak magnetic fields

This paper is devoted to the derivation of the dispersion relation of the Geodesic Acoustic Mode in pair plasmas, i.e. assuming that ions and electrons have the same mass. Geodesic Acoustic Modes are plasma perturbations playing a crucial role in turbulence regulation, and therefore in the determination of the plasma confinement in tokamaks. Experiments with pair plasmas, like electron-positron plasmas, have been proposed with different kinds of confinements, and aim to study fundamental processes in plasma physics and understanding the formation of the early universe.

physics.plasm-ph↗

arXiv2026

Horizon-Aware Early Event Prediction for Tokamak Disruption Alarms

Reliable disruption prediction is essential for the safe operation of future tokamaks. Existing full-distribution survival methods model the complete residual time-to-disruption distribution, whereas operational decisions primarily depend on disruption risk within a finite prediction horizon. This mismatch motivates introducing Early Event Prediction (EEP) objectives into survival-based disruption prediction. We take Deep Survival Machines (DSM) as the full-distribution baseline and propose applying two established EEP methods to tokamak disruption prediction: Temporal Label Smoothing (TLS), which directly predicts disruption probability within a finite horizon, and survTLS, which additionally models the event-time distribution within that horizon. Using a common causal encoder, we compare these methods on DIII-D, Alcator C-Mod, and EAST. We distinguish threshold-free deadline ranking from validation-selected fixed-policy alarm performance and evaluate prediction horizons and encoder architectures. TLS achieves the best mean alarm performance on DIII-D and EAST, whereas all methods perform poorly on Alcator C-Mod. survTLS does not consistently outperform DSM, suggesting that directly learning horizon-level event probability is more effective than modeling detailed within-horizon event-time distributions in the present setting. Finally, the selected prediction horizons and encoder-ablation results vary across devices, reflecting differences in disruption characteristics.

physics.plasm-ph↗