KRR can potentially achieve higher accurate than NN for small databases. On the other hand, NN is accurate and fast for large training databases. Since KRR becomes computationally demanding with increasing database size, training an ML model on energy gradient/forces, i.e., 3𝑁-times more variables, seems more suitable for NN. However, several KRR-based methods suitable for GPU/TPU also exist (e.g., QML-lightning 97 and sGDML ). For now, JKML allows for the training of the above-mentioned NN-based potential utilizing the forces extracted from QC via JKQC. The trained model can be used for geometry optimizations and fast MD simulations. However, for accurate and fast modeling, the training database must be constructed for the system at hand. Additionally, the greater the number of atom types in the studied system, the greater the required training database.