Computational-assisted catalyst design facilitates identification of a potent non-intuitive fluorinated acridinium catalyst for aerobic photoredox conversion of polystyrene to benzoic acid.
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.
In the past decade, halogen bonding (XB) has been utilized extensively in the design of novel materials and new drugs. One of the emerging applications of XB is in devising organic room-temperature phosphorescent materials. Several reports showed that the use of benzaldehyde-based phosphors with appropriate XB donors results in high phosphorescence quantum yields because of enhanced intersystem crossing (kISC) and phosphorescence (kPH) rates. It is often advocated that a combination of factors, namely, rigidification, heavy-atom effect, and reduction of quenching by triplet dioxygen, is responsible for the enhancement. However, to what extent each factor contributes to the enhancement is unknown. In this study, we performed ab initio excited-state calculations on two XB complexes, benzaldehyde···XF (X = Br and I), with varying XB distance to elucidate the effect of XB on kISC and kPH. Our results show that XB reduces the kISC of benzaldehyde and changes the character of T₁ from which phosphorescence takes place. Hence, the generally accepted assumption that XB enhances spin–orbit coupling and kISC is oversimplified.
Abstract 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.
In the past decade, halogen bonding (XB) has been utilized extensively in the design of novel materials and new drugs. One of the emerging applications of XB is in devising organic room-temperature phosphorescent materials. Several reports showed that the use of benzaldehyde-based phosphors with appropriate XB donors results in high phosphorescence quantum yields because of enhanced intersystem crossing (kISC) and phosphorescence (kPH) rates. It is often advocated that a combination of factors, namely, rigidification, heavy-atom effect, and reduction of quenching by triplet dioxygen, is responsible for the enhancement. However, to what extent each factor contributes to the enhancement is unknown. In this study, we performed ab initio excited-state calculations on two XB complexes, benzaldehyde···XF (X = Br and I), with varying XB distance to elucidate the effect of XB on kISC and kPH. Our results show that XB reduces the kISC of benzaldehyde and changes the character of T1 from which phosphorescence takes place. Hence, the generally accepted assumption that XB enhances spin–orbit coupling and kISC is oversimplified.
Training and validation dataset of 325,535 unique canonical SMILES without stereochemistry from COCONUT database, January 2022 version (Accessed on 1 August 2022)
This work provides a computation-driven investigation of the stability of organic electrolytes for lithium-air batteries. Electrolyte instability is currently a key challenge that limits practical use of aprotic Li-air batteries, and the chemical processes that cause this instability are often kinetically-driven. Computational screening for kinetic stability involves the determination of reaction barriers for the numerous potential reaction mechanisms, barriers that are challenging to calculate due to the difficulty of locating transition state structures. Here we screen a broad set of substituted electrolytes for susceptibility to nucleophilic attack by superoxide. We find that carbonates are not typically expected to be stable and that sulfones are generally stable, validating literature trends. We study the effects of chemical functionalization with electron-donating and withdrawing groups and their interplay with steric factors, identifying functional groups and other chemical modifications that increase stability in these groups. User-input driven transition state identification is used for these initial calculations, and an automated computational pipeline is subsequently presented and validated as a means to perform further high-throughput searches across mechanisms and chemistries. The pipeline integrates cheminformatics-based reaction encoding, relaxed potential energy scans, and nudged elastic band calculations for an end-to-end approach to barrier calculations. We review this automated search approach and its current limitations, and discuss challenges and further work.
<div>Modeling dynamical effects in chemical reactions, such as post-transition state bifurcation, requires <i>ab initio</i> molecular dynamics simulations due to the breakdown of simpler static models like transition state theory. However, these simulations tend to be restricted to lower-accuracy electronic structure methods and scarce sampling because of their high computational cost. Here, we report the use of statistical learning to accelerate reactive molecular dynamics simulations by combining high-throughput ab initio calculations, graph-convolution interatomic potentials and active learning. This pipeline was demonstrated on an ambimodal trispericyclic reaction involving 8,8-dicyanoheptafulvene and 6,6-dimethylfulvene. With a dataset size of approximately</div><div>31,000 M062X/def2-SVP quantum mechanical calculations, the computational cost of exploring the reactive potential energy surface was reduced by an order of magnitude. Thousands of virtually costless picosecond-long reactive trajectories suggest that post-transition state bifurcation plays a minor role for the reaction in vacuum. Furthermore, a transfer-learning strategy effectively upgraded the potential energy surface to higher</div><div>levels of theory ((SMD-)M06-2X/def2-TZVPD in vacuum and three other solvents, as well as the more accurate DLPNO-DSD-PBEP86 D3BJ/def2-TZVPD) using about 10% additional calculations for each surface. Since the larger basis set and the dynamic correlation capture intramolecular non-covalent interactions more accurately, they uncover longer lifetimes for the charge-separated intermediate on the more accurate potential energy surfaces. The character of the intermediate switches from entropic to thermodynamic upon including implicit solvation effects, with lifetimes increasing with solvent polarity. Analysis of 2,000 reactive trajectories on the chloroform PES shows a qualitative agreement with the experimentally-reported periselectivity for this reaction. This overall approach is broadly applicable and opens a door to the study of dynamical effects in larger, previously-intractable reactive systems.</div>
Abstract 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.
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