Machine learning can be a powerful tool to prescreen new materials, thus accelerating the development of the OPV field.

Overhead cranes are widely used in industrial applications for material displacing. Many linear or non‐linear control schemes have been proposed for overhead cranes and implemented on electronic systems, but energy efficiency of transportation has seldom been considered in motion planning. This study aims at finding an optimal solution of motion planning in terms of energy efficiency for overhead cranes. Using the optimal control method an optimal trajectory is obtained with less energy consumption than the compared trajectories and is also satisfying physical and practical constraints such as swing, acceleration and jerk. Besides the energy optimal model, the authors also propose two other models to optimise time efficiency and safety during transportation. The results obtained have been compared with some existing motion trajectories, and have been shown to be superior to these benchmarks in terms of energy efficiency, time efficiency and safety, respectively.