To validate the generalization of our method, we tested two different NN architectures, as discussed in the section on default parameter values. The first architecture is shallower, with the number of nodes in each encoder layer set to [15, 40, 20, 5, 1], while the second is deeper and wider, with nodes configured as [15, 90, 40, 2, 20, 1]. For bond detection in Equation ( The results of the deeper NN confirm that our method is valid in both reaction directions. Figure demonstrates that even with a simpler NN structure, our approach remains effective. Initially, learning the symmetric and asymmetric properties of the reaction is more challenging. However, with the aid of the skewness-based loss term and the biased wall, a rough approximation of the reaction path is established, enabling the system to quickly reach alternative states due to the stronger wall.