Interactive molecular dynamics in virtual reality for modelling materials and catalysts

Multilevel quantum mechanical calculations show the role of promoter molecules in the dehydration of methanol to dimethyl ether in H-ZSM-5

Methyl carboxylate esters promote the formation of dimethyl ether (DME) from the dehydration of methanol in H-ZSM-5 zeolite. We employ a multilevel quantum method to explore the possible associative and dissociative mechanisms in the presence, and absence, of six methyl ester promoters. This hybrid method combines density functional theory, with dispersion corrections (DFT-D3), for the full periodic system, with second-order Møller-Plesset perturbation theory (MP2) for small clusters representing the reaction site, and coupled cluster with single, double, and perturbative triple substitution (CCSD(T)) for the reacting molecules. The calculated adsorption enthalpy of methanol, and reaction enthalpies of the dehydration of methanol to DME within H-ZSM-5, agree with experiment to within chemical accuracy (∼4 kJ mol-1). For the promoters, a reaction pathway via an associative mechanism gives lower overall reaction enthalpies and barriers compared to the reaction with methanol only. Each stage of this mechanism is explored and related to experimental data. We provide evidence that suggests the promoter's adsorption to the Brønsted acid site is the most important factor dictating its efficiency.

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview

Artificial Neural Networks (ANNs) are transforming how we understand chemical mixtures, providing an expressive view of the chemical space and multiscale processes. Their hybridization with physical knowledge can bridge the gap between predictivity and understanding of the underlying processes. This overview explores recent progress in ANNs, particularly their potential in the 'recomposition' of chemical mixtures. Graph-based representations reveal patterns among mixture components, and deep learning models excel in capturing complexity and symmetries when compared to traditional Quantitative Structure-Property Relationship models. Key components, such as Hamiltonian networks and convolution operations, play a central role in representing multiscale mixtures. The integration of ANNs with Chemical Reaction Networks and Physics-Informed Neural Networks for inverse chemical kinetic problems is also examined. The combination of sensors with ANNs shows promise in optical and biomimetic applications. A common ground is identified in the context of statistical physics, where ANN-based methods iteratively adapt their models by blending their initial states with training data. The concept of mixture recomposition unveils a reciprocal inspiration between ANNs and reactive mixtures, highlighting learning behaviors influenced by the training environment.

Computational Insight of Phase Transformation and Drug Release Behaviour of Doxycycline-Loaded Ibuprofen-Based In-Situ Forming Gel

This research investigates the gel formation behaviour and drug-controlling performance of doxycycline-loaded ibuprofen-based in-situ forming gels (DH-loaded IBU-based ISGs) for potential applications in periodontal treatment. The investigation begins by exploring the physical properties and gel formation behaviour of the ISGs, with a particular focus on determining their sustained release capabilities. To gain a deeper understanding of the molecular interactions and dynamics within the ISGs, molecular dynamic (MD) simulations are employed. The effects of adding IBU and DH on reducing surface tension and water tolerance properties, thus affecting molecular properties. The phase transformation phenomenon is observed around the interface, where droplets of ISGs move out to the water phase, leading to the precipitation of IBU around the interface. The optimization of drug release profiles ensures sustained local drug release over seven days, with a burst release observed on the first day. Interestingly, different organic solvents show varying abilities to control DH release, with dimethyl sulfoxide (DMSO) demonstrating superior control compared to N-Methyl-2-pyrrolidone (NMP). MD simulations using AMBER20 software provide valuable insights into the movement of individual molecules, as evidenced by root-mean-square deviation (RMSD) values. The addition of IBU to the system results in the retardation of IBU molecule movement, particularly evident in the DMSO series, with the diffusion constant value of DH reducing from 1.2452 to 0.3372 and in the NMP series from 0.3703 to 0.2245 after adding IBU. The RMSD values indicate a reduction in molecule fluctuation of DH, especially in the DMSO system, where it decreases from over 140 to 40 Å. Moreover, their radius of gyration is influenced by IBU, with the DMSO system showing lower values, suggesting an increase in molecular compactness. Notably, the DH-IBU configuration exhibits stable pairing through H-bonding, with a higher amount of H-bonding observed in the DMSO system, which is correlated with the drug retardation efficacy. These significant findings pave the way for the development of phase transformation mechanistic studies and offer new avenues for future design and optimization formulation in the ISG drug delivery systems field.