Fullerene fragments, referred to as buckybowls, are garnering interest due to their distinctive molecular shapes and optoelectronic properties. Here, we report the synthesis and characterization of a novel C70 subunit, diindeno­[4,3,2,1-fghi:4′,3′,2′,1′-opqr]­perylene, that is substituted with either triethylsilyl­(TES)-ethynyl or 2,4,6-triisopropylphenyl groups at the meta-positions. The resulting compounds (1 and 2) display a bowl-to-bowl inversion at room temperature. Notably, the substituent groups on the meta-positions alter both the geometric and the electronic properties as well as the crystal packing of the buckybowls. In contrast to the 2,4,6-triisopropylphenyl groups in 2, the TES-ethynyl groups in 1 lead to enhanced bond length alternation, resulting in weaker aromaticity of the six-membered rings of the buckybowl skeleton. 1 forms one-dimensional (1D) concave-in-convex stacking columns, and when 1 is blended with C70, the buckybowls encapsulate C70 and result in two-dimensional cocrystals. Organic field-effect transistor (OFET) measurements demonstrate that 1 displays a hole mobility of 0.31 cm2 V–1 s–1, and the 1-C70 cocrystal exhibits ambipolar transport characteristics with electron and hole mobilities approaching 0.40 and 0.07 cm2 V–1 s–1, respectively. This work demonstrates the potential of buckybowls for the development of organic semiconductors.

A novel propellane rylene imide is synthesized. It serves as a versatile building block for quasi-D3h symmetric nanostructures.

Genetic algorithms (GA) and machine learning (ML) have a long history of development and use in chemistry. Recent algorithmic and computational advances, however, have brought these methods to the forefront of chemical research, and chemistry is experiencing a transformation in the way that machines and humans interact to pursue scientific advances. The field of materials chemistry, in particular, has witnessed a considerable expansion in the maturity of GA and ML approaches, as machine-based materials design ushers in a new era of materials development, discovery, and deployment. In addition to predicting new compositions and properties of bulk materials, GA and ML have also guided new insights into the structure, composition, and chemistry of materials surfaces. In this review, we focus on how GA and ML have been used in conjunction with chemical simulation techniques to advance understanding of surface chemistry, examining the history, recent work, and overall success of these applications.

Layered double hydroxides (LDH) demonstrate significant potential across a range of applications, including as catalysts, delivery vehicles for pharmaceuticals, environmental remediation, and supercapacitors. Explaining the mechanism of LDH action at the atomic scale in these and other applications is challenging, however, due to the difficulty in precisely defining the bulk and surface structure and chemical compositions. Here, we focus on the determination of the structure of lithium–aluminum (Li–Al) LDH, which has shown promise in the catalytic depolymerization of lignin, both directly as the catalyst and as a support for gold nanoparticles. While the relative positions of the Li and Al metals are generally well resolved by X-ray crystallography, it is the structures of the anionic layers, consisting of water and carbonate, that are less well established. Combinatorial analyses of all possible positions and rotations of the water and carbonate in the three-layered Li-AL LDH polytope reveals that the phase space is much too large to examine in any reasonable time frame in a one-by-one structure exploration. To overcome this limitation, we develop and deploy a genetic algorithm (GA) wherein fitness is determined by matching a calculated X-ray diffraction (XRD) pattern for a given structure to the known experimental XRD pattern. The GA approach results in structures of high fitness that portend the bulk Li–Al LDH structure. Importantly, the GA approach offers the potential to determine the structures of other LDH, and more generally layered materials, which are generally difficult to describe given the large chemical and structural space to be explored.