A path-based boosting algorithm for exploring transition metal compounds

Motivated by the analysis of chemical metal compounds and their properties for catalysis, we developed a gradient boosting model that explores graph structures to perform prediction tasks. Taking advantage of the iterative nature of boosting, our novel approach, called PathBoost , explores the graphs to identify the relevant paths and simultaneously fits a prediction model. Advantages of PathBoost include automatic variable selection, as only relevant paths are kept in the model and explainability, as a measure of variable importance is provided. The novel algorithm is applied to the tmQM dataset, where the molecules are represented as graphs with atoms as nodes and bonds as edges. The goal is to predict specific quantum properties of the molecules, such as the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) gap. These properties usually require heavy computational power to be computed, while our model aims to provide comparable results using much fewer resources.