Enhancing the photocatalytic efficiency of two-dimensional aluminum nitride materials through strategic rare earth doping.

Two-dimensional (2D) materials demonstrate promising potential as high-efficiency photocatalysts. However, the intrinsic limitations of aluminum nitride (AlN), such as inadequate oxidation capacity, a high carrier recombination rate, and limited absorption of visible light, pose considerable challenges. In this paper, we introduce a novel co-doping technique with dysprosium (Dy) and carbon (C) on a 2D AlN monolayer, aiming to enhance its photocatalytic properties. Our first-principles calculations reveal a reduction in the bandgap and a significant enhancement in the visible light absorption rate of the co-doped Al24N22DyC2 structure. Notably, the distribution of the highest occupied molecular orbital and the lowest unoccupied molecular in proximity to Dy atoms demonstrates favorable conditions for carrier separation. Theoretical assessments of the hydrogen evolution reaction and oxygen evolution reaction activities further corroborate the potential of Al24N22DyC2 as a competent catalyst for photocatalytic reactions. These findings provide valuable theoretical insights for the experimental design and fabrication of novel, high-efficiency AlN semiconductor photocatalysts.

Predict the Polarizability and Order of Magnitude of Second Hyperpolarizability of Molecules by Machine Learning.

In order to determine the polarizability and hyperpolarizability of a molecule, several key parameters need to be known, including the excitation energy of the ground and excited states, the transition dipole moment, and the difference of dipole moment between the ground and excited states. In this study, a machine-learning model was developed and trained to predict the molecular polarizability and second-order hyperpolarizability on a subset of QM9 data set. The density of states was employed as input to the model. The results demonstrated that the machine-learning model effectively estimated both polarizability and the order of magnitude of second-order hyperpolarizability. However, the model was unable to predict the dipole moment and first-order hyperpolarizability, suggesting limitations in its ability to predict the difference of dipole moment between the ground and excited states. The computational efficiency of machine-learning models compared to traditional quantum mechanical calculations enables the possibility of large-scale screening of molecules that satisfy specific requirements using existing databases. This work presents a potential solution for the efficient exploration and analysis of molecules on a larger scale.

Novel Third-Order Nonlinear Optical Materials with Craig-Möbius Aromaticity.

Electron delocalization in aromatic materials significantly impacts their third-order nonlinear optics (NLO). Despite organometallic complexes with Craig-Möbius aromaticity attracting great attention for their unusual physicochemical properties, their third-order NLO have been little studied to date. Herein, 12 Craig-Möbius aromatic organometallics with a stable structure similar to osmapentalyne, namely, carbolong complexes, are screened by DFT. They exhibit high third-order NLO responses because of the d and p electron delocalization in the organometallic ring. Furthermore, electron-hole distribution analyses draw a conclusion that extending the conjugated plane will increase the π-conjugation system to enhance the local excitation in the plane, and the introduction of typical aromatic ligands can result in the organometallic ring-to-ligand charge transfer (RLCT), which are effective methods to improve the third-order NLO response. This study opens a new window in the application of Craig-Möbius aromatic complexes and provides a new approach for third-order NLO materials design.

Tin Metal Cluster Compounds as New Third-Order Nonlinear Optical Materials by Computational Study.

It is quite appealing but challenging to predict and synthesize new nonlinear optical (NLO) materials with exceptional performance. Herein, the different Sn4 cluster core structures and third-order NLO properties are studied through electronic structure, excited hole-electron, bonding character, and aromaticity analysis. As a result, Sn4 clusters with ring core structure (Sn4-R) not only have the smallest Egap, the largest UV-vis response intensity, but also the strongest third-order NLO response in our work. As proved by natural bond orbitals' (NBO) analysis, electron localization function (ELF), and adaptive natural density partitioning (AdNDP), the Sn44+ has two in-plane four center-two electron (4c-2e) Sn-Sn σ-bonds, resulting in a good delocalization. For the first time, delocalization of metal cluster cores in tin clusters that is beneficial to the third-order NLO response is proposed, which provides a new guidance to design and prepare third-order NLO materials.