LAUSR

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 use case, the ML results are used to augment the chemistry of a BCl$_3$/H$_2$ gas mixture.