The most popular two ML potentials are ANI (with versions of ANI-1, ANI-2x, and ANI-2xt) and AIMNET, which could be successfully utilized for energy minimization and low-lying minimum search using several gradient based minimization algorithms . A general issue with ML potential is that the PES is not expressed in an explicit mathematical function form. As a result, its smoothness depends heavily on the quality of the training data, and it may not be as smooth or physically interpretable as the PES from QM or MM methods. Having a non-smooth PES raises problems in gradient-based minimizations as they can easily be stuck on saddle-points where the energy derivation with respect to coordinate is zero. While such challenges are inherent to both smooth and non-smooth potential energy surfaces (PES), non-smoothness worsens the problem by introducing abrupt discontinuities in PES, often arising from undertrained ML models with insufficient or low-quality data. This issue is particularly manifested for geometries far from equilibrium, where sampling is sparse.