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Molecular electronics is often limited by the poorly defined nature of the contact between the molecules and the metal surface. We describe a method to wire molecules into gaps in single-walled carbon nanotubes (SWNTs). Precise oxidative cutting of a SWNT produces carboxylic acid-terminated electrodes separated by gaps of

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The electrocatalytic reduction of CO2 into value-added chemicals such as hydrocarbons has the potential for supplying fuel energy and reducing environmental hazards, while the accurate tuning of electrocatalysts at the ultimate single-atomic level remains extremely challenging. In this work, we demonstrate an atomic design of multiple oxygen vacancy-bound, single-atomic Cu-substituted CeO2 to optimize the CO2 electrocatalytic reduction to CH4. We carried out theoretical calculations to predict that the single-atomic Cu substitution in CeO2(110) surface can stably enrich up to three oxygen vacancies around each Cu site, yielding a highly effective catalytic center for CO2 adsorption and activation. This theoretical prediction is consistent with our controlled synthesis of the Cu-doped, mesoporous CeO2 nanorods. Structural characterizations indicate that the low concentration (<5%) Cu species in CeO2 nanorods are highly dispersed at single-atomic level with an unconventionally low coordination number ∼5, suggesting the direct association of 3 oxygen vacancies with each Cu ion on surfaces. This multiple oxygen vacancy-bound, single atomic Cu-substituted CeO2 enables an excellent electrocatalytic selectivity in reducing CO2 to methane with a faradaic efficiency as high as 58%, suggesting strong capabilities of rational design of electrocatalyst active centers for boosting activity and selectivity.

Recent proposals of topological flat band (TFB) models have provided a new route to realize the fractional quantum Hall effect (FQHE) without Landau levels. We study hard-core bosons with short-range interactions in two representative TFB models, one of which is the well known Haldane model (but with different parameters). We demonstrate that FQHE states emerge with signatures of even number of quasi-degenerate ground states on a torus and a robust spectrum gap separating these states from higher energy spectrum. We also establish quantum phase diagrams for the filling factor 1/2 and illustrate quantum phase transitions to other competing symmetry-breaking phases. Introduction.-The fractional quantum Hall effect (FQHE), one of the most fascinating discoveries in twodimensional (2D) electron gas, has set up a paradigm to explore new topological phases in other strongly correlated systems. As commonly believed, the FQHE requires two basic ingredients: single-particle states with nontrivial topology, and quenching of the kinetic energy compared to interaction energy scale. However, despite of the seemingly universal theoretical concepts, the FQHE has only been found in 2D systems under a strong perpendicular magnetic field, i.e., in which particles move in Landau levels (LLs). In rotating Bose-Einstein condensate [1] and optical lattice systems [2,3], researchers have been interested in generating an artificial uniform magnetic field, thus the bosonic FQHE states are expected, but still due to the existence of LLs.

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)­emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.