Atomic distortion and the repulsive interactions of the noble gas atoms

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.

Does the Intra-Atomic Deformation Energy of Interacting Quantum Atoms Represent Steric Energy?

We show that the mutual, through-space compression of atomic volume experienced by approaching topological atoms causes an exponential increase in the intra-atomic energy of those atoms, regardless of approach orientation. This insight was obtained using the modern energy partitioning method called interacting quantum atoms (IQA). This behaviour is consistent for all atoms except hydrogen, which can behave differently depending on its environment. Whilst all atoms experience charge transfer when they interact, the intra-atomic energy of the hydrogen atom is more vulnerable to these changes than larger atoms. The difference in behaviour is found to be due to hydrogen's lack of a core of electrons, which, in heavier atoms, consistently provide repulsion when compressed. As such, hydrogen atoms do not always provide steric hindrance. In accounting for hydrogen's unusual behaviour and demonstrating the exponential character of the intra-atomic energy in all other atoms, we provide evidence for IQA's intra-atomic energy as a quantitative description of steric energy.

Optimized unlike-pair interactions for water-carbon dioxide mixtures described by the SPC/E and EPM2 models

The unlike-pair interaction parameters for the SPC/E-EPM2 models have been optimized to reproduce the mutual solubility of water and carbon dioxide at the conditions of liquid-supercritical fluid phase equilibria. An efficient global optimization of the parameters is achieved through an implementation of the coupling parameter approach, adapted to phase equilibria calculations in the Gibbs ensemble, that explicitly corrects for the overpolarization of the SPC/E water molecule in the nonpolar CO(2) environments. The resulting H(2)O-CO(2) force field accurately reproduces the available experimental solubilities at the two fluid phases in equilibria as well as the corresponding species tracer diffusion coefficients.

Transferable Potentials for Phase Equilibria. 7. Primary, Secondary, and Tertiary Amines, Nitroalkanes and Nitrobenzene, Nitriles, Amides, Pyridine, and Pyrimidine

The transferable potentials for phase equilibria (TraPPE) force fields are extended to amine, nitro, nitrile, and amide functionalities and to pyridine and pyrimidine. In many cases, the same parameters for a functional group are used for both united-atom and explicit-hydrogen representations of alkyl tails. Following the TraPPE philosophy, the nonbonded interaction parameters were fitted to the vapor-liquid coexistence curves for selected one-component systems. Coupled-decoupled configurational-bias Monte Carlo simulations in the Gibbs ensemble were applied to neat (methyl-, dimethyl-, trimethyl-, ethyl-, diethyl-, or triethyl-)amine, nitromethane, nitroethane, nitrobenzene, acetonitrile, propionitrile, acetamide, propanamide, butanamide, pyridine, and pyrimidine. Excellent agreement with experimental results was found, with the mean unsigned errors being less than 1% for both the critical temperature and the normal boiling temperature. Similarly, the liquid densities at low reduced temperatures are reproduced to within 1%, and the deviation for the critical densities is about 4%. Additional simulations were performed for the binary mixtures of methylamine + n-hexane, diethyl ether + acetonitrile, 1-propanol + acetonitrile, and nitroethane + ethanol. With the exception of the methylamine/n-hexane mixture for which the separation factor is substantially overestimated, agreement with experiment for the other three mixtures is very satisfactory.