Figure ) use OCP20 database , JARVIS-DFT and MP databases as examples for developing domain specific and unified MLP models. Although the OCP20 database contains hundreds of millions of images and took hundreds of billions of CPU-core-hours to construct, it is free and open, so researchers do not need to spend additional resources to prepare training datasets . But also because the training dataset is very large, the computational budget required to train a model ranges from hundreds to tens of thousands of GPU-hours, which is also the bottleneck of adopting these approaches. fields (MLFF)/machine learning potentials (MLP), have been developed for specific systems, recently a unified models were developed that can predict energies and forces of structurally and chemically diverse material systems across the periodic table for 89 elements. Such models include ALIGNN-FF , MATGL , CHGNet and MACE .