At this small molecular size, the computational cost of DFT is relatively low, meaning the practical speed-up offered by ML is minimal. Moreover, these datasets consist of much smaller molecules than those used in practical applications, such as pharmaceutical discovery. Evaluating the performance of these architectures on larger molecules, covering more diverse and chemically relevant spaces, would be a valuable direction for future research.