Recently, we proposed a double-ended method called the direct MaxFlux method. The original MaxFlux method is a variational reaction path optimization designed to incorporate temperature effects. However, we repurposed it for TS searches by setting the temperature close to zero. The MaxFlux method is advantageous because it is (i) variational, (ii) converges to the exact minimum energy path in the zero-temperature limit, and (iii) does not include energy derivatives in its objective function. In our previous study, 9 we implemented the MaxFlux method using two numerical techniques: directly evaluating the objective function by expanding the path with B-spline functions and adaptively updating the energy evaluation points. This approach enables TS searches with fewer energy evaluations and iterations, reducing the total computational cost by approximately half compared to the NEB method. 12, The Python implementation of the direct MaxFlux method is available on GitHub. One common difficulty in double-ended methods is atomic collision along the reaction path, as strong repulsive forces arising from short interatomic distances can lead to diver-gence in calculations. These collisions are primarily caused by poor initial paths, such as linear interpolation between reactant and product structures. Various techniques have been proposed to avoid atomic collisions, including the growing string method, 7 which gradually optimizes from both ends of the path, and methods that generate collision-free plausible paths using lightweight calculations. For instance, the image-dependent pair potential (IDPP), a molecular structure-based potential energy, enables rapid generation of collision-free paths while preserving non-reactive bonds when used with the NEB method. Another approach is to generate a reaction path as a geodesic on a Riemannian manifold with an appropriate Riemannian metric. Paths obtained from these methods often serve as initial paths for optimization with precise potential energies. IDPP, in particular, is widely used as implemented in packages like the Atomic Simulation Environment (ASE). Although IDPP yields high-quality paths for simple molecules, problems arise as the system becomes complex. The first problem is molecular distortion caused by redundant constraints. IDPP imposes constraints on all interatomic distances so that they vary linearly from product to reactant. However, linear interpolation of interatomic distances does not guarantee a sterically favorable path. Moreover, the number of atomic pairs greatly exceeds the system's degrees of freedom, causing unresolvable frustration. This can lead to undesired distortions in the molecular structure. To alleviate the redundancy, IDPP reduces the constraints when interatomic distances are long. However, this leads to the second problem: an excessive stabilization of long interatomic distances, including undesired bond breakings. In complex systems, this vulnerability to bond breaking often proves fatal. In this study, we propose a novel molecular structure-based potential energy that reduces undesired distortions and bond breakings. This potential energy is expressed as a sum of (piecewise) quadratic functions of interatomic distances for each atom pair, resembling elastic network models (ENMs) often used in biomolecular models. The present potential energy has an"image-independent" flat bottom consisting of the structures of reactant and product, and other atomic collision-free structures preserving non-reactive bonds, bond angles, and planar structures. The path optimization within this flat bottom is not subject to frustration causing molecular distortions. Moreover, since each pair potential is single-welled and does not stabilize long interatomic distances, the present potential energy is robust against undesired bond breaking.