Ultrafast Photocontrolled Rotation in a Molecular Motor Investigated by Machine Learning-Based Nonadiabatic Dynamics Simulations

Ultrafast dynamics in spatially confined photoisomerization: accelerated simulations through machine learning models

This study sheds light on the exploration of photoresponsive host-guest systems, highlighting the intricate interplay between confined spaces and photosensitive guest molecules. Conducting nonadiabatic molecular dynamics (NAMD) simulations based on electronic structure calculations for such large systems remains a formidable challenge. By leveraging machine learning (ML) as an accelerator for NAMD simulations, we analytically constructed excited-state potential energy surfaces along relevant collective variables to investigate photoisomerization processes efficiently. Combining the quantum mechanics/molecular mechanics (QM/MM) methodology with ML-based NAMD simulations, we elucidated the reaction pathways and identified the key degrees of freedom as reaction coordinates leading to conical intersections. A machine learning-based nonadiabatic dynamics model has been developed to compare the excited-state dynamics of the guest molecule, benzopyran, in both the gas phase and its behavior within the confined space of cucurbit[5]uril. This comparative analysis was designed to determine the influence of the environment on the photoisomerization rate of the guest molecule. The results underscore the effectiveness of ML models in simulating trajectory evolution in a cost-effective manner. This research offers a practical approach to accelerate NAMD simulations in large-scale systems of photochemical reactions, with potential applications in other host-guest complex systems.

On the supramolecular interactions into a pH- and Metal-Actuated Molecular Shuttle: some insights from QTAIM modeling

Supramolecular contacts responsible for chemical interaction of cucurbit[7]uril (CB[7]) macrocycle on a Tolyl-Viologen-Phenylene-Imidazole (T-VPI) molecular thread, at acid pH (T-VPI-H+) or after Ag+ cation addition (T-VPI-Ag+), are analytically addressed in a computational framework combining Quantum Theory of Atoms in Molecules (QTAIM) with Density Functional Theory (DFT). In this respect, the crystallographic structure (CCDC number 2217466) is taken as reference condition for addressing the nature of the chemical interactions driving the shuttling of the CB[7] between T and P stations recently observed in dilute water solutions. Beside the host(CB[7]) vs guest(T-VPI-H+ or T-VPI-Ag+) complexation, the coordination sphere of the Ag+ cation is also investigated by means of local electronic energy density - H(r) - descriptors. The derived non-covalent interaction patterns are found to support diagnostic 1H NMR signals used for detecting the mutual position of the CB[7] along the axle. This work highlights the potentialities of a QTAIM based approach in the characterization of supramolecular and metal-complexation effects in molecular aggregates such as not-interlocked synthetic molecular shuttles.

Theoretical trends in the dynamics simulations of molecular machines across multiple scales

Over the past few decades, molecular machines have been extensively studied, since they are composed of single molecules for functional materials capable of responding to external stimuli, enabling motion at scales ranging from the microscopic to the macroscopic level within molecular aggregates. This advancement holds the potential to efficiently transform external resources into mechanical movement, achieved through precise control of conformational changes in stimuli-responsive materials. However, the underlying mechanism that links microscopic and macroscopic motions remains unclear, demanding computational development associated with simulating the construction of molecular machines from single molecules. This bottleneck has impeded the design of more efficient functional materials. Advancements in theoretical simulations have successfully been developed in various computational models to unveil the operational mechanisms of stimulus-responsive molecular machines, which could help us reduce the costs in experimental trial-and-error procedures. It opens doors to the computer-aided design of innovative functional materials. In this perspective, we have reviewed theoretical approaches employed in simulating dynamic processes involving conformational changes in molecular machines, spanning different scales and environmental conditions. In addition, we have highlighted current challenges and anticipated future trends in the collective control of aggregates within molecular machines. Our goal is to provide a comprehensive overview of recent theoretical advancements in the field of molecular machines, offering valuable insights for the design of novel smart materials.