For predefined descriptors, one option is making modifications to the ACSFs. In 2017, Smith et al. developed ANAKIN-ME (ANI) for general off-equilibrium organic molecules . They modified the angular term in ACSF to improve performance over diverse datasets. TensorMol adopts ANI's descriptor and includes an atomic charge prediction network to incorporate long-range interaction. Similarly, wACSF , sin-gleNN and other methods are also based on Behler's ACSFs. Deep potential MD (DeepMD) and its smooth version (DeepPot-SE) developed by 2018 introduced deep potential as another type of predefined descriptors. Deep potential defines a local coordinate frame based on the two closest neighboring atoms. In 2019, Zhang and coworkers developed the embedded atom neural network (EANN) and introduce descriptors based on an empirical embedded atom method (EAM) . Other types of setups include pair-based descriptors . A concern in the use of predefined descriptors is on the choice of selected hyperparameters. A poor selection may result in limited resolution of atomic displacement with degenerate predicted values .