RESUMEN
Inspired by the recent interest of halogen bonding (XB) in the solid state, we detail a comprehensive benchmark study of planewave DFT geometry and interaction energy of lone-pair (LP) type and aromatic (AR) type halogen bonded complexes, using PAW and USPP pseudopotentials. For LP-type XB dimers, PBE-PAW generally agrees with PBE/aug-cc-pVQZ(-pp) geometries but significantly overbinds compared to CCSD(T)/aug-cc-pVQZ(-pp). Grimme's D3 dispersion corrections to PBE-PAW gives better agreement to the MP2/cc-pVTZ(-pp) results for AR-type dimers. For interaction energies, PBE-PAW may overbind or underbind for weaker XBs but clearly overbinds for stronger XBs. D3 dispersion corrections exacerbate the overbinding problem for LP-type complexes but significantly improves agreement for AR-type complexes compared to CCSD(T)/CBS. Finally, for periodic XB crystals, planewave PBE methods slightly underestimate the XB lengths by 0.03 to 0.05 Å. © 2019 Wiley Periodicals, Inc.
RESUMEN
The recently developed adiabatic absolutely localized molecular orbital energy decomposition analysis (ALMO-EDA) has proven to be useful in determining the effects of different energy components on the geometries of complexes bound by intermolecular interactions. The authors have applied it to systems such as the water dimer, water-ion complexes, metallocenes and lone-pair type halogen-bonded (XB) dimers. In this study, we have extended the second-generation ALMO-EDA method to 40 different XB complexes by benchmarking against its classical counterpart and symmetry-adapted perturbation theory (SAPT). In addition, we have examined the nature of halogen bonding involving less studied XB acceptors, namely π-systems, radicals and carbenes, using the adiabatic ALMO-EDA analyses, particularly to shed light on how each energy component affects the geometries of the XB complexes. Our results show that the second-generation ALMO-EDA predicts a higher electrostatic energy contribution in all XB complexes compared to SAPT and classical ALMO-EDA schemes. On the other hand, when comparing across different XB acceptors, all three partition schemes produced the same qualitative finding. The adiabatic ALMO-EDA analyses indicate that while the inclusion of a charge transfer contribution is important in achieving accurate XB bond lengths and interaction energies, as well as recovering the binding site specificity of XB involving benzene and naphthalene acceptors, it is sufficient to obtain the linearity of the XB complexes in the frozen approximation.
RESUMEN
Halogen bonding (XB) has become one of the most studied non-covalent interactions in the past two decades, owing to its wide range of applications in materials and biological applications. Most of the current theoretical and experimental studies focus on XB involving lone-pair acceptors due to its predictability in terms of crystal geometries. However, recent reports have advocated the importance of XB materials involving aromatic-type acceptors because of their relevance in functional materials, catalysis and biological systems. Herein, we report the XB site-specificity in several polycyclic aromatic hydrocarbons (PAHs) and N-heteroaromatic compounds that are ubiquitous in chemical systems. Based on a series of quantum chemical studies of Cl2 and Br2 XB complexes with 14 representative systems, these XB sites can be easily predicted using occupied molecular orbitals and atomic charges. We envisage that the predicted site maps will be useful for materials and drug design involving this class of non-covalent interactions.
RESUMEN
Converting polystyrene into value-added oxygenated aromatic compounds is an attractive end-of-life upcycling strategy. However, identification of appropriate catalysts often involves laborious and time-consuming empirical screening. Herein, after demonstrating the feasibility of using acridinium salts for upcycling polystyrene into benzoic acid by photoredox catalysis for the first time, we applied low-cost descriptor-based combinatorial in silico screening to predict the photocatalytic performance of a family of potential candidates. Through this approach, we identified a non-intuitive fluorinated acridinium catalyst that outperforms other candidates for converting polystyrene to benzoic acid in useful yields at low catalyst loadings (≤5 mol%). In addition, this catalyst also proved effective with real-life polystyrene waste containing dyes and additives. Our study underscores the potential of computer-aided catalyst design for valorizing polymeric waste into essential chemical feedstock for a more sustainable future.
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Two most common crystal structures in metals and metal alloys are body-centered cubic (bcc) and face-centered cubic (fcc) structures. The phase transitions between these structures play an important role in the production of durable and functional metal alloys. Despite their technological significance, the details of such phase transitions are largely unknown because of the challenges associated with probing these processes. Here, we describe the nanoscopic details of an fcc-to-bcc phase transition in PdCu alloy nanoparticles (NPs) using in situ heating transmission electron microscopy. Our observations reveal that the bcc phase always nucleates from the edge of the fcc NP, and then propagates across the NP by forming a distinct few-atoms-wide coherent bcc-fcc interface. Notably, this interface acts as an intermediate precursor phase for the nucleation of a bcc phase. These insights into the fcc-to-bcc phase transition are important for understanding solid - solid phase transitions in general and can help to tailor the functional properties of metals and their alloys.
RESUMEN
Natural products are a rich resource of bioactive compounds for valuable applications across multiple fields such as food, agriculture, and medicine. For natural product discovery, high throughput in silico screening offers a cost-effective alternative to traditional resource-heavy assay-guided exploration of structurally novel chemical space. In this data descriptor, we report a characterized database of 67,064,204 natural product-like molecules generated using a recurrent neural network trained on known natural products, demonstrating a significant 165-fold expansion in library size over the approximately 400,000 known natural products. This study highlights the potential of using deep generative models to explore novel natural product chemical space for high throughput in silico discovery.