Alternatively, to improve model performance with limited data, a common practice is to utilize specialized features-oftentimes computed descriptors-that are more relevant to the targets of interest. However, the best choice of descriptors is often task-specific, and obtaining these descriptors can require substantial effort. For instance, Sigman et al. utilized multivariate linear regression to establish a correlation between electronic and steric descriptors and reaction properties, including electrochemical potential, enantio-, site-, and chemo-selectivity. Doyle and co-workers employed random forest to predict reaction yields of C-N cross-coupling reactions by selecting mechanistically relevant descriptors. Boobier et al. employed a limited number of descriptors to predict solubility in both organic solvents and water. This approach of selecting targeted descriptors has gained more traction and application in recent years, with a growing inclination towards using quantum mechanical (QM) descriptors. This shift is driven by the belief that QM descriptors can provide deeper physical insights and enhance model interpretability. Additionally, they are considered more extensible and generalizable across a wide range of chemical species and property prediction tasks. The significance of representation in chemical property prediction models has led to a growing interest in recent studies to develop methods for obtaining better representations.