Exploration of quantitative structure–property relationships (QSPR) for the design of new guanidinium ionic liquids

Rationalization of the thermal properties of some polycyclic organophosphorus compounds by structural and QSPR analyses

In this work, we establish quantitative structure-property relationships (QSPR) to predict the melting temperatures of diphosphine dioxides possessing a tetradecahydrophosphanthrene core, based on four two-parameter models. Three new compounds were synthesized using the Bouveault-Blanc reduction of appropriate tertiary phosphine oxides, and these compounds exhibit high melting temperatures (approximately 280-410 °C). Models were built using a dataset of 13 compounds, including the new ones. It was found that models 1 and 2, based on molecular volume, yield satisfactory results for the entire dataset, whereas models 3 and 4, based on the calculated lattice energy from crystal structures, provide even better predictions for all compounds. The new compounds were characterized by X-ray structural analysis, differential scanning calorimetry, and theoretical calculations. Cluster analysis indicates that, for crystal stability, van der Waals forces are as important as Coulombic interactions in stabilizing the crystal lattice.

Insights into the quantitative structure-activity relationship for ionic liquids: a bibliometric mapping analysis

Environmental protection and sustainability is the development goal that countries all over the world are pursuing. Ionic liquids (ILs), as a new type of green material, have a great application prospect. And the quantitative structure-activity relationship (QSAR) is significant for the research of ILs. To better understand the role played by QSAR in the research of ILs, 4139 literatures published in the WOS database from 2002 to 2022 were used for bibliometric analysis, and different types of knowledge maps were mapped to obtain the current status and trends of IL research applied QSAR. The distribution pattern of the literature output chronology, country, institution, author cooperation, and major source journals can be obtained through the research of the distribution of literature. Through core literature, dual-map overlays, and evolutionary path analysis, the research knowledge base was obtained mainly including ionic liquid toxicological properties research, environmental protection and sustainability, ionic liquid design, and mild steel corrosion inhibition; through the co-occurrence and evolution of keywords, the current research hotspots are basic properties of ILs, corrosion inhibition of mild steel, the effect of toxicity on the environment, QSAR modeling methods, solvent application of ILs, and drug design.

Review of Neural Network Modeling of Shape Memory Alloys

Shape memory materials are smart materials that stand out because of several remarkable properties, including their shape memory effect. Shape memory alloys (SMAs) are largely used members of this family and have been innovatively employed in various fields, such as sensors, actuators, robotics, aerospace, civil engineering, and medicine. Many conventional, unconventional, experimental, and numerical methods have been used to study the properties of SMAs, their models, and their different applications. These materials exhibit nonlinear behavior. This fact complicates the use of traditional methods, such as the finite element method, and increases the computing time necessary to adequately model their different possible shapes and usages. Therefore, a promising solution is to develop new methodological approaches based on artificial intelligence (AI) that aims at efficient computation time and accurate results. AI has recently demonstrated some success in efficiently modeling SMA features with machine- and deep-learning methods. Notably, artificial neural networks (ANNs), a subsection of deep learning, have been applied to characterize SMAs. The present review highlights the importance of AI in SMA modeling and introduces the deep connection between ANNs and SMAs in the medical, robotic, engineering, and automation fields. After summarizing the general characteristics of ANNs and SMAs, we analyze various ANN types used for modeling the properties of SMAs according to their shapes, e.g., a wire as an actuator, a wire with a spring bias, wire systems, magnetic and porous materials, bars and rings, and reinforced concrete beams. The description focuses on the techniques used for NN architectures and learning.

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

Computer-Aided Molecular Design of Ionic Liquids: An Overview

Computer-aided molecular design (CAMD) is a technique that helps select potential target molecules that will have desired properties before synthesis and testing in the laboratory, and provides an excellent complement to the chemical intuition possessed by experimentalists. Property predictions are obtained from a quantitative structure–property relationship (QSPR) that links changes at the molecular structure level to differences in the macroscopic properties. Ionic liquids (ILs) offer an excellent opportunity for the application of CAMD because of the numerous possible combinations of cations and anions available to fine-tune physical properties. In addition, there are many innovative applications of ILs where CAMD could make an impact. In this overview, we present the general methodology for CAMD with QSPR, and describe recent progress in this area related to ILs.