RESUMO
We report that a newly developed type of triaryltriazine rotor, which bears bulky silyl moieties on the para position of its peripheral phenylene groups, forms a columnar stacked clutch structure in the crystalline phase. The phenylene units of the crystalline rotors display two different and interconvertible correlated molecular motions. It is possible to switch between these intermolecular geared rotational motions via a thermally induced crystal-to-crystal phase transition. Variable-temperature solid-state 2H NMR measurements and X-ray diffraction studies revealed that the crystalline rotor is characterized by a vertically stacked columnar structure upon introducing a bulky Si moiety with bent geometry as the stator. The structure exhibits correlated flapping motions via a combination of 85° and ca. 95° rotations between 295 and 348 K, concurrent with a negative entropy change (ΔS = -23 ± 0.3 cal mol-1 K-1). Interestingly, heating the crystal beyond 348 K induces an anisotropic expansion of the column and lowers the steric congestion between the adjacent rotators, thus altering the correlated motions from a flapping motion to a correlated 2-fold 180° rotation with a lower entropic penalty (ΔS = -14 ± 0.5 cal mol-1 K-1). The obtained results of our study suggest that the intermolecular stacking of the C3-symmetric rotator driven by the steric repulsion of the bulky stator represents a promising strategy for producing various correlated molecular motions in the crystalline phase. Moreover, direct and reversible modulation of the intermolecularly correlated rotation is achieved via a thermally induced crystal-to-crystal phase transition, which operates as a gearshift function at the molecular level.
RESUMO
Nuclear magnetic resonance (NMR) spectroscopy is one of the indispensable techniques in chemistry because it enables us to obtain accurate information on the chemical, electronic, and dynamic properties of molecules. Computational simulation of the NMR spectra requires time-consuming density functional theory (DFT) calculations for an ensemble of molecular conformations. For large flexible molecules, it is considered too high-cost since it requires time-averaging of the instantaneous chemical shifts of each nuclear spin across the conformational space of molecules for NMR timescales. Here, we present a Gaussian process/deep kernel learning-based machine learning (ML) method for enabling us to predict, average in time, and analyze the instantaneous chemical shifts of conformations in the molecular dynamics trajectory. We demonstrate the use of the method by computing the averaged 1H and 13C chemical shifts of each nuclear spin of a trefoil knot molecule consisting of 24 para-connected benzene rings (240 atoms). By training ML model with the chemical shift data obtained from DFT calculations, we predicted chemical shifts for each conformation during dynamics. We were able to observe the merging of the time-averaged chemical shifts of each nuclear spin in a singlet 1H NMR peak and two 13C NMR peaks for the knot molecule, in agreement with experimental measurements. The unique feature of the presented method is the use of the learned low-dimensional deep kernel representation of local spin environments for comparing and analyzing the local chemical environment histories of spins during dynamics. It allowed us to identify two groups of protons in the knot molecule, which implies that the observed singlet 1H NMR peak could be composed of the contributions from protons with two distinct local chemical environments.
