A Variational Finite Element Discretization of Compressible Flow

Foundations of Computational Mathematics - We present a finite element variational integrator for compressible flows. The numerical scheme is derived by discretizing, in a structure-preserving way,...     

Adhesion between graphite and modified polyester surfaces: a theoretical study

This study examines the adhesion of graphite to functionalized polyester surfaces using a range of qualitative and quantitative measures of theoretical adhesion. Modifications to the polyester surfaces include the addition of hydroxyl, carboxyl, or fluorine substituents with coverages of 0.4 and 0.9 groups per nm(2). In each case, the introduction of substituents to the surface of the polyester was calculated to lead to reduced adhesion to graphite. Effects of surface relaxation on adhesion are studied by employing different simulation protocols. The theoretical results suggest one mechanism to reduce adhesion to carbonaceous solids is to increase atomic roughness using strongly hydrophilic or alternatively strongly hydrophobic substituents.     

Fast 3D shape screening of large chemical databases through alignment-recycling

Background  

Large chemical databases require fast, efficient, and simple ways of looking for similar structures. Although such tasks are now fairly well resolved for graph-based similarity queries, they remain an issue for 3D approaches, particularly for those based on 3D shape overlays. Inspired by a recent technique developed to compare molecular shapes, we designed a hybrid methodology, alignment-recycling, that enables efficient retrieval and alignment of structures with similar 3D shapes.     

Homolytic reactions of polyfluoroaromatic compounds V. The reaction of pentafluorophenyl radicals with fluorobenzene

The arylation of fluorobenzene with pentafluorophenyl radicals obtained by the reaction of the corresponding aniline with pentyl nitrite is described. The reaction involves attact at the 2t?-,3t?-and 4t?-positions. The composition of the mixture and the nature of its components were determined by ¹⁹F NMR spectroscopy and were confirmed by gas-liquid chromatography.     

BonDNet: a graph neural network for the prediction of bond dissociation energies for charged molecules

A broad collection of technologies, including e.g. drug metabolism, biofuel combustion, photochemical decontamination of water, and interfacial passivation in energy production/storage systems rely on chemical processes that involve bond-breaking molecular reactions. In this context, a fundamental thermodynamic property of interest is the bond dissociation energy (BDE) which measures the strength of a chemical bond. Fast and accurate prediction of BDEs for arbitrary molecules would lay the groundwork for data-driven projections of complex reaction cascades and hence a deeper understanding of these critical chemical processes and, ultimately, how to reverse design them. In this paper, we propose a chemically inspired graph neural network machine learning model, BonDNet, for the rapid and accurate prediction of BDEs. BonDNet maps the difference between the molecular representations of the reactants and products to the reaction BDE. Because of the use of this difference representation and the introduction of global features, including molecular charge, it is the first machine learning model capable of predicting both homolytic and heterolytic BDEs for molecules of any charge. To test the model, we have constructed a dataset of both homolytic and heterolytic BDEs for neutral and charged (−1 and +1) molecules. BonDNet achieves a mean absolute error (MAE) of 0.022 eV for unseen test data, significantly below chemical accuracy (0.043 eV). Besides the ability to handle complex bond dissociation reactions that no previous model could consider, BonDNet distinguishes itself even in only predicting homolytic BDEs for neutral molecules; it achieves an MAE of 0.020 eV on the PubChem BDE dataset, a 20% improvement over the previous best performing model. We gain additional insight into the model''s predictions by analyzing the patterns in the features representing the molecules and the bond dissociation reactions, which are qualitatively consistent with chemical rules and intuition. BonDNet is just one application of our general approach to representing and learning chemical reactivity, and it could be easily extended to the prediction of other reaction properties in the future.

Prediction of bond dissociation energies for charged molecules with a graph neural network enabled by global molecular features and reaction difference features between products and reactants.     

General approach for the synthesis of chiral perylenequinones via catalytic enantioselective oxidative biaryl coupling

By using oxygen as the terminal oxidant, copper complexes derived from chiral 1,5-diaza-cis-decalin catalyze the enantioselective oxidative biaryl coupling of highly functionalized naphthols to provide octa- and decasubstituted binaphthalenes with high selectivity (86-90% ee). Products containing very electron-rich naphthalenes were prone to epimerization under the reaction conditions. This epimerization could be suppressed by employing naphthol starting materials with phenol protecting groups that attenuated the electron-rich nature of the naphthalenes. Direct oxidation of the resultant chiral 1,1'-binaphthol framework completed the first asymmetric synthesis of a perylenequinone containing only an axial chirality element.