Machine learning for plasma reaction kinetic data estimation

One of the most common challenges in the plasma modeling community is the limited availability of crucial data, including cross-sections and reaction rate coefficients, which serve as inputs for plasma models. Typically, chemistry sets used in plasma modeling contain many different species involved in a significant number of reactions, many of which lack reported kinetic parameters. This missing data can be obtained experimentally or calculated from first principles; however, both of these approaches are extremely time-consuming and not always feasible. A cost-effective alternative to these expensive methods is to use machine learning techniques, which can provide reasonable estimates of missing kinetic data in a short period of time.