  Stars' chemical signatures provide invaluable insights into stellar cluster
formation. This study utilized the Weisfeiler-Lehman (WL) Graph Kernel to
examine a 15-dimensional elemental abundance space. Through simulating chemical
distributions using normalizing flows, the effectiveness of our algorithm was
affirmed. The results highlight the capability of the WL algorithm, coupled
with Gaussian Process Regression, to identify patterns within elemental
abundance point clouds correlated with various cluster mass functions. Notably,
the WL algorithm exhibits superior interpretability, efficacy and robustness
compared to deep sets and graph convolutional neural networks and enables
optimal training with significantly fewer simulations (O(10)), a reduction of
at least two orders of magnitude relative to graph neural networks.
