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The catalytic transformation of the heavy aromatics in bitumen into lighter components is the key to the upgrading and refining of the oil sands. To understand the chemical bonding in molybdenum carbide nanoparticle (MCNP) catalysts and the chemisorption bonds between the MCNPs and unsaturated hydrocarbons, the topological analysis of the electron localization function was applied to various MCNPs and their complexes with unsaturated hydrocarbons. For some of the smaller complexes, comparisons are made with the atoms in molecules approach, including the calculation of delocalization indices. The results are interpreted in the Lewis bonding scheme. It was found that the Mo-C bonding can be highly ionic in cases like Mo8C12 and MoC but shows significant covalent character in Mo2C, Mo3C, and Mo28C14. The chemisorption bonds between hydrocarbons and the MCNPs involve electron sharing of various types with strong covalent character. The strong three- or four-center interactions determine the adsorption configurations of the hydrocarbons on the MCNPs. Derivatives of benzene show some different bonding features, which depend strongly on the substituent or the heteroatom. [ABSTRACT FROM AUTHOR]

Finding the optimum material with improved properties for a given application is challenging because data acquisition in materials science and chemistry is time consuming and expensive. Therefore, dealing with small datasets is a reality in chemistry, whether the data are obtained from synthesis or computational experiments. In this work, we propose a new artificial intelligence method based on active learning (AL) to guide new experiments with as little data as possible, for optimum experimental design. The AL method is applied to ABO3 perovskites, where a descriptor based on atomic properties was developed. Several regressor algorithms were employed: artificial neural network, Gaussian process, and support vector regressor. The developed AL method was applied in the experimental design of two important materials: non-stoichiometric perovskites (Ba(1−x)AxTi(1−y)ByO3) due to substituting ionic sites with different concentrations and elements (A = Ca, Sr, Cd; B = Zr, Sn, Hf), aiming at the maximization of the energy storage density, and stoichiometric ABO3 perovskites where different elements are changed in the A and B sites for the minimization of the formation energy. AL for experimental design is implemented in the machine learning agent for chemistry and design (MLChem4D) software, which has the potential to be applied in inorganic and organic synthesis (e.g., search for the optimum concentrations, catalysts, reactants, temperatures, and pH to improve the yield) and materials science (e.g., search the periodic table for the proper elements and their concentrations to improve the materials properties). The latter marks the first MLChem4D application for the design of perovskites. [ABSTRACT FROM AUTHOR]

The structures and stabilities of the glycerol (G) and glycerol 3-phosphate (G3P) isomers have been calculated in the gas phase, using an ab initio density functional theory (DFT) method. The different conformational structures are shown to be at the origin of the various phospholipid conformers, except, obviously, for the alkyl chain torsions. The G3P conformations have been examined taking into account the experimental structures of the complexation sites in the glycerol kinase (GK) and glycerol 3-phosphate acyl transferase (G3PAT) enzymes, which correspond to the first and second steps of the “de novo” phospholipid biogenesis, respectively. The conformational analysis of the glycerophosphate skeleton is shown to determine most of the structural characteristics of the phosphatidyl choline lipids, which differ by the length of their diacyl chains, i.e., dilauroyl (DL), dimyristoyl (DM), and dipalmitoyl (DP) phosphatidylcholines (PC). Higher energy conformers with kinks in the acyl chains have been found, in preparation for molecular dynamics studies of the chain melting phase transformation. [ABSTRACT FROM AUTHOR]