On-the-Fly Training of Atomistic Potentials for Flexible and Mechanically Interlocked Molecules

Mechanically interlocked molecules have gained significant attention because of their unique ability to perform well-defined motions originating from their entanglement, which is important for the design of artificial molecular machines. Atomistic simulations based on force fields (FFs) provide detailed insights into such architectures at the molecular level enabling one to predict the resulting functionalities. However, the development of reliable FFs is still challenging and time-consuming, in particular for highly dynamic and interlocked structures such as rotaxanes, which exhibit a large number of different conformers. In the present work, we present an on-the-fly training (OTFT) algorithm. By a guided and nonguided phase space sampling, relevant reference data are automatically and continuously generated and included for the on-the-fly parametrization of the FF based on a population swapping genetic algorithm (psGA). The OTFT approach provides a fast and automated FF parametrization scheme and tackles problems caused by missing phase space information or the need for big data. We demonstrate the high accuracy of the developed FF for flexible molecules with respect to equilibrium and out-of-equilibrium properties. Finally, by applying the ab initio parametrized FF, molecular dynamic simulations were performed up to experimentally relevant time scales (ca. 1 μs) enabling capture in detail of the structural evaluation and mapping out of the free-energy topology. The on-the-fly training approach thus provides a strong foundation toward automated FF developments and large-scale investigations of phenomena in and out of thermal equilibrium.