This work is done to understand the mechanistic details of the excited state non-adiabatic process of diazirine and three other derivatives that are substituted with electron-donating (methoxy-diazirine) and withdrawing (fluoro-diazirine) groups, as well as combining both (fluoro-methoxy-diazirine), to study their effect on generating carbene and molecular nitrogen via diazomethane. All electronic structure calculations are performed at SA2-CASSCF followed by MS-CASPT2 levels of theory, and non-adiabatic molecular dynamics simulations at SA2-CASSCF level by choosing appropriate active spaces. Two conical intersections (CIs) are found for each of the molecules in the non-adiabatic process, which are associated with asymmetric vibrations of the C─N bonds in the three-membered ring of the diazirines, except for pure diazirine, where the second CI is associated with symmetric vibration. The dynamics starts from n-π* excited state (S1). In the dynamics, trajectories are seen to go through both the CIs whichever is energetically and dynamically permissible. The first-order kinetics of N2 formation revealed that the reaction is fastest for pure diazirine and slowest for fluoro-methoxy-diazirine. The mechanism, as revealed from the present calculation, looks so reliable that an MS-CASPT2 dynamics, if performed, would perhaps change the population in each pathway, leading to different rate constants of N2 formation, but the number of pathways and the trend of rate constants may remain the same.
The cover artwork vividly illustrates a novel computational framework, integrating high-throughput calculations and machine learning, to accelerate the design of refractory multi-principal element alloys. The glowing spheres represent the constituent refractory elements, which form the vast compositional space for exploration. This space is systematically navigated by the central funnel, symbolizing the study✀s powerful pipeline that combines Exact Muffin-Tin Orbitals with Coherent Potential Approximation (EMTO-CPA) calculations with a copula entropy-driven machine learning model. The framework successfully predicts stable alloy compositions, represented by the resulting body-centered cubic crystal structure. The complex spectra and the vibrant property landscape below symbolize the accurate prediction of key mechanical properties, such as elastic constants, guiding the discovery of tailored alloys with exceptional high-temperature stability for extreme environments. More details can be found in the Research Article by Kai Xiong, Aimin Zhang, Yingwu Wang, and co-workers (DOI: 10.1002/adts.202500784).
This study explores the effects of a spatially varying magnetic field on the flow and heat transfer of a power-law non-Newtonian fluid past a stretchable porous wedge within a Darcy–Forchheimer porous medium. A mathematical framework is developed by extending the Navier–Stokes and energy equations, incorporating the Cauchy stress tensor for power-law fluids, Darcy's law, Forchheimer's inertial correction, and Lorentz force effects. The governing partial differential equations are transformed into a system of nonlinear ordinary differential equations via a local non-similar transformation, treating dimensionless parameters as streamwise independent. The resulting system is solved using a finite difference method (FDM) in MATLAB, generating 200 data points for velocity, temperature, skin friction, and Nusselt number. These results are further employed to train a supervised artificial intelligence model based on the Levenberg-Marquardt Scheme–Artificial Neural Network (LMS-ANN), with 70% of data used for training and 15% each for validation and testing. Comparative analysis demonstrates excellent agreement between FDM and LMS-ANN, achieving mean squared errors as low as 2.6 × 10−7 for skin friction and 6.6 × 10−8 for Nusselt number. The study confirms that Darcy–Forchheimer effects significantly enhance velocity and heat transfer, establishing the LMS-ANN framework as a reliable and efficient predictive tool.
Pseudomonas aeruginosa, a notorious gram-negative pathogen, exhibits extensive antibiotic resistance and is a prominent cause of nosocomial infections. Given the urgency of addressing this challenge, exploring therapeutic alternatives to mitigate virulence factors is imperative. This study utilizes in silico analysis to probe the interactions between synthetic autoinducers and pivotal proteins (LasR, QscR, RhlR, and PqsR) within P. aeruginosa Quorum Sensing network. Unveiling distinct binding modes and active site selectivity, it sheds light on protein-specific regulation design for tailored therapies. LasR and RhlR demonstrate stabilization through flexible complex structures, with LasR favoring π-ion and π–π stacking interactions, potentially linked to hydrophobic pocket modifications, while RhlR relies on hydrogen bonds for stability. In contrast, QscR and PqsR prefer long side chains and π-type interactions, altering the hydrophobic pocket without inducing flexibility, with minimal hydrogen bond formation. These insights underscore the potential for personalized therapeutic interventions targeting bacterial virulence factor.
The bandgap is a fundamental parameter that governs the electronic and optoelectronic properties of materials. However, accurately predicting this parameter remains a significant challenge due to the inherent trade-off between computational efficiency and model interpretability. This study introduces a bandgap correction framework based on genetic-programming symbolic regression. The approach integrates analytical formulations with physical priors and is trained on PBE and HSE06 bandgap data of quaternary chalcogenides computed within the framework of density functional theory. It provides an accurate linear correction to PBE bandgaps, aligning them closely with HSE06 predictions, while maintaining high computational efficiency and clear physical interpretability. Furthermore, the model also demonstrates strong generalization capabilities across multiple isostructural space groups. Beyond its predictive accuracy, the model uncovers an interpretable relationship between the bandgap and underlying electronic structure, thereby offering a transparent and generalizable strategy for band structure engineering in multicomponent semiconductors.
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