A Regression Model for Plasma Reaction Kinetics

Machine learning (ML) is used to provide reactions rates appropriate for models of low temperature plasmas with a focus on A + B $\rightarrow$ C + D binary chemical reactions. The regression model is trained on data extracted from the QBD, KIDA, NFRI and UfDA databases. The regression model used a variety of data on the reactant and product species, some of which also had to be estimated using ML. The final model is a voting regressor comprising three distinct optimized regression models: a support vector regressor, random forest regressor and a gradient-boosted trees regressor model; this model is made freely available via a GitHub repository. As a sample use case, the ML results are used to augment the chemistry of a BCl3/H2 gas mixture

LiDB: Database of molecular radiative lifetimes for plasma processes

LiDB is a newly developed database of molecular vibrational and vibronic state radiative lifetimes. It has been created with the aim of enabling radiative effects to be properly captured in low-temperature plasma models. Datasets have been generated for 36 molecules using comprehensive and highly accurate molecular line lists from the ExoMol spectroscopic database. The main data output of LiDB is radiative lifetimes at vibrational state resolution. Partial lifetimes, which give information on the dominant decay channels in a molecule, are also provided. LiDB is freely available to the scientific community and is hosted at \href{www.exomol.com/lidb}{www.exomol.com/lidb}. Users can dynamically view molecular datasets or use a specially-designed application programming interface (API) to make data requests. LiDB will continue to expand in the future by adding more molecules, important isotopologues, and neutral and singly-charged atomic species

Quantemol Electron Collisions (QEC): An Enhanced Expert System for Performing Electron Molecule Collision Calculations Using the R-Matrix Method

Collisions of low energy electrons with molecules are important for understanding many aspects of the environment and technologies. Understanding the processes that occur in these types of collisions can give insights into plasma etching processes, edge effects in fusion plasmas, radiation damage to biological tissues and more. A radical update of the previous expert system for computing observables relevant to these processes, Quantemol-N, is presented. The new Quantemol Electron Collision (QEC) expert system simplifyies the user experience, improving reliability and implements new features. The QEC graphical user interface (GUI) interfaces the Molpro quantum chemistry package for molecular target setups, and the sophisticated UKRmol+ codes to generate accurate and reliable cross-sections. These include elastic cross-sections, super elastic cross-sections between excited states, electron impact dissociation, scattering reaction rates, dissociative electron attachment, differential cross-sections, momentum transfer cross-sections, ionization cross sections, and high energy electron scattering cross-sections. With this new interface we will be implementing dissociative recombination estimations, vibrational excitations for neutrals and ions, and effective core potentials in the near future

Targeted Cross-Section Calculations for Plasma Simulations

Gathering data on electron collisions in plasmas is a vital part of conducting plasma simulations. However, data on neutral radicals and neutrals formed in the plasma by reactions between different radicals are usually not readily available. While these cross-sections can be calculated numerically, this is a time-consuming process and it is not clear from the outset which additional cross-sections are needed for a given plasma process. Hence, identifying species for which additional cross-sections are needed in advance is highly advantageous. Here, we present a structured approach to do this. In this, a chemistry set using estimated data for unknown electron collisions is run in a global plasma model. The results are used to rank the species with regard to their influence on densities of important species such as electrons or neutrals inducing desired surface processes. For this, an algorithm based on graph theory is used. The species ranking helps to make an informed decision on which cross-sections need to be calculated to improve the chemistry set and which can be neglected to save time. The validity of this approach is demonstrated through an example in an SF6/O2 plasma

Targeted Cross-Section Calculations for Plasma Simulations

Gathering data on electron collisions in plasmas is a vital part of conducting plasma simulations. However, data on neutral radicals and neutrals formed in the plasma by reactions between different radicals are usually not readily available. While these cross-sections can be calculated numerically, this is a time-consuming process and it is not clear from the outset which additional cross-sections are needed for a given plasma process. Hence, identifying species for which additional cross-sections are needed in advance is highly advantageous. Here, we present a structured approach to do this. In this, a chemistry set using estimated data for unknown electron collisions is run in a global plasma model. The results are used to rank the species with regard to their influence on densities of important species such as electrons or neutrals inducing desired surface processes. For this, an algorithm based on graph theory is used. The species ranking helps to make an informed decision on which cross-sections need to be calculated to improve the chemistry set and which can be neglected to save time. The validity of this approach is demonstrated through an example in an SF6/O2 plasma