In conclusion, by addressing these challenges-ranging from the rapid proliferation of data sets to the need for standardized formats and long-term accessibility-the future of quantum chemical databases and data sets holds immense promise. The integration of active learning, high-throughput calculations, and other advanced methodologies will further enhance the utility of these resources, enabling ML potentials to reach their full potential in computational chemistry. This synergy between data and algorithms will accelerate scientific discovery, driving progress across a wide array of fields and deepening our understanding of the molecular world.