Computational design of light-driven molecular nanomotors

Enrico Tapavicza, Michelle Menkel-Lantz, Arjang Farhim

Light-driven molecular nanomotors (MNMs) [1] convert light into mechanical energy. Due to their temporal and spatial controllability, MNMs have several potential applications in biomedical sciences, including drug delivery and permeabilization of membranes [2]. Improving their efficiency requires optimization of various molecular properties. Above all, a high product quantum yield (PQY) [3] for the photochemical isomerization that drive the conversion of photon energy into mechanical work is needed. This typically goes in hand with a high absorption cross section. Due to improved tissue penetration, near-IR light is the desired source of energy. However, due to the excitation energy required to drive the isomerization, this often requires usage of two-photon absorption. Based on quantum chemistry and ab initio non-adiabatic molecular dynamics [4], I will report efforts to design new MNMs with improved PQY, one- and two-photon absorption cross sections. To allow screening of a large number of molecular candidates, machine learning methods for the prediction of excited state properties [5, 6] take a crucial role in this development.

Figure 1. Photoinduced isomerization of a unidirectional MNM, simulated by ab initio non-adiabatic molecular dynamics.

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