However, it is important to note that different databases employ varying DFT calculators, convergence conditions, parameter settings, and energy references, which makes it possible for even the same system to have different adsorption energies. Consequently, it becomes challenging to utilize all public databases simultaneously to train a universal model. Figure illustrates the above three cases. Figure (a) shows problem-specific case, which requires a relatively small training dataset (≈1,000 systems). However, since it is problem-specific, in most cases, researchers have to prepare the database by themselves, which can take several hundreds of CPU-core-hours for DFT calculations. Even with the help of supercomputers, this is still an expensive task. However, a relatively small training dataset makes training ML models very cheap. Even using a personal computer, training can be completed in a few GPU-hours.