Density functional theory (DFT) computations apply to physics, chemistry, material science, and engineering. In chemical engineering, DFT identifies material structure and properties, and mechanisms for phenomena such as chemical reaction and phase transformation that are otherwise impossible to measure experimentally. Even though its practical application dates back only a decade or two, it is already a standard tool for materials modelling. Many textbooks and articles describe the theoretical basis of DFT, but it remains difficult for researchers to autonomously learn the steps to accurately calculate system properties. Here, we first explain the foundations of DFT in a way accessible to chemical engineers with little background in quantum mechanics or solid‐state physics. Then, we introduce the basics of the computations and, for most of the rest of the article, we show how to derive physical characteristics of interest to chemical engineers: elastic, thermodynamic, and surface properties, electronic structure, and surface and chemical reaction energy. Finally, we highlight some limitations of DFT; since these calculations are approximations to the Schrödinger equation, their accuracy relies on choosing adequate exchange‐correlation functions and basis sets. Since 1991, the number of articles WoS has indexed related to DFT has increased quadratically with respect to time and now numbers 15 000. A bibliometric analysis of the top 10 000 cited articles in 2018 and 2019 classifies them into four clusters: adsorption, graphene, and nanoparticles; ab initio molecular dynamics and crystal structure; electronic structure and optical properties; and total energy calculations and wave basis sets.

Ionic liquid mixtures with both different cations and anions (i.e., ternary reciprocal mixtures) are often formally treated as binary mixtures. Mixing laws for binary mixtures are inappropriate for ternary reciprocal mixtures as they do not account for both attractive and repulsive interactions between ions in those liquids. In this work, the viscosity of the [C 2 py], [C 4 py] // Cl, Br ternary reciprocal system (where [C n py] = 1-alkylpyridinium) and all its common-ion binary and unary subsystems was measured over the entire composition range from temperatures close to the liquidus up to about 200 °C. A new viscosity model was proposed to describe the viscosity of ternary reciprocal mixtures more rigorously by accounting for all ion−ion interactions. The robustness of the proposed viscosity model was discussed in comparison with other approaches proposed in the literature. Anomalous discrepancies for the low-temperature viscosity data were observed close to the center of the reciprocal square (consisting of an equimolar mixture of the four pure salts [C 2 py]Cl, [C 2 py]Br, [C 4 py]Cl, and [C 4 py]Br) and could not be accounted for by any of the approaches considered.

No abstract

Review of principles and limitations of viscosity models for ionic liquids and their mixtures focusing on the use of inappropriate mixing rules for molten salts.

The modeling of hydrogen solubility in multicomponent Al-(Li, Mg, Cu, and Si) liquid phase has been performed with a thermodynamic approach using the modified quasichemical model with the pair approximation (MQMPA). All hydrogen solubility data available in literature was assessed critically to obtain the binary parameters of the MQMPA model for the Al-H, Li-H, Mg-H, Cu-H, Zn-H, and Si-H melts. For the Li-H system, a new thermodynamic description of the stable solid lithium hydride was determined based on the c p found in literature. The thermodynamic model for the Al-Li system also was reassessed in this work to take into account the short-range ordering observed for this system. Built-in interpolation techniques allow the model to estimate the thermodynamic properties of the multicomponent liquid solution from the liquid model parameters of the lower order subsystems. A comparison of the calculated hydrogen solubility performed at various equilibrium conditions of temperature, pressure, and composition with the available experimental data found in the literature is presented in this work, as well as a comparison with some results from previous modeling.