Computational Methods for Predicting Physicochemical Properties of Ionic Liquids and Deep Eutectic Solvents
- 1st Edition - May 1, 2027
- Latest edition
- Editors: Saeid Atashrouz, Abdolhossein Hemmati-Sarapardeh, Ahmad Mohaddespour
- Language: English
Computational Methods for Predicting Physicochemical Properties of Ionic Liquids and Deep Eutectic Solvents presents a comprehensive overview of computational, theoretical, and AI… Read more
Description
Description
Computational Methods for Predicting Physicochemical Properties of Ionic Liquids and Deep Eutectic Solvents presents a comprehensive overview of computational, theoretical, and AI-driven approaches for predicting and designing the physicochemical properties of ionic liquids (ILs) and deep eutectic solvents (DES). It systematically discusses classical modeling methods such as group contribution techniques, equations of state, and semi-empirical correlations alongside modern machine learning and molecular simulation frameworks. By comparing the accuracy, reliability, and computational efficiency of these predictive models across key properties including density, viscosity, thermal conductivity, surface tension, heat capacity, electrical conductivity, and gas solubility, the book enables readers to select the most appropriate approach for their research or industrial needs. In addition, it integrates case studies and practical resources, including tutorials, datasets, and simulation guides, making it a valuable reference for students, researchers, and professionals working in chemical engineering, physical chemistry, and materials science.
Key features
Key features
- Offers comprehensive coverage of various predictive approaches, including machine learning, group contribution methods, equations of state, semi-empirical correlations, and molecular dynamic simulations, for determining the physicochemical properties of ionic liquids and DES
- Provides In-depth analysis and comparison of models for properties such as density, viscosity, thermal conductivity, surface tension, heat capacity, electrical conductivity, and gas solubility, highlighting their accuracy, reliability, and computational efficiency
- Offers detailed comparison of predictive techniques theoretical, semi-theoretical, correlative, group contribution, molecular simulation, and ML models including their accuracy, reliability, and computational cost
- Provides practical tools and resources through appendices containing simulation guides, flowcharts, tutorials, and datasets for hands-on learning
Readership
Readership
Scientists and graduate students in the fields of physical chemistry, chemical engineering, and materials engineering
Table of contents
Table of contents
1. Introduction to Ionic Liquids
2. Theoretical, Semi-Theoretical, Correlative, and Group Contribution Methods
3. Machine Learning and Metaheuristic Algorithms
4. Density of Ionic Liquids
5. Viscosity of Ionic Liquids
6. Thermal Conductivity of Ionic Liquids
7. Surface Tension of Ionic Liquids
8. Heat Capacity of Ionic Liquids
9. Electrical Conductivity of Ionic Liquids
10. Ultrasonic Investigations of Pure Ionic Liquids
11. Refractive Index of Ionic Liquids
12. Solubility of Gases in Ionic Liquids
13. Properties of Mixtures Containing Ionic Liquids
14. Toxicity of Ionic Liquids
15. Economic Aspects and Process Simulations for Ionic Liquid-Based Applications
16. Properties of Deep Eutectic Solvents (DES)
2. Theoretical, Semi-Theoretical, Correlative, and Group Contribution Methods
3. Machine Learning and Metaheuristic Algorithms
4. Density of Ionic Liquids
5. Viscosity of Ionic Liquids
6. Thermal Conductivity of Ionic Liquids
7. Surface Tension of Ionic Liquids
8. Heat Capacity of Ionic Liquids
9. Electrical Conductivity of Ionic Liquids
10. Ultrasonic Investigations of Pure Ionic Liquids
11. Refractive Index of Ionic Liquids
12. Solubility of Gases in Ionic Liquids
13. Properties of Mixtures Containing Ionic Liquids
14. Toxicity of Ionic Liquids
15. Economic Aspects and Process Simulations for Ionic Liquid-Based Applications
16. Properties of Deep Eutectic Solvents (DES)
Product details
Product details
- Edition: 1
- Latest edition
- Published: May 1, 2027
- Language: English
About the editors
About the editors
SA
Saeid Atashrouz
Saeid Atashrouz (born 1990, Dezful, Iran) received his PhD in Chemical Engineering from Amirkabir University of Technology (Tehran Polytechnic) in 2020. His research covers a broad spectrum of topics in chemical engineering, with a particular emphasis on process modeling, optimization, and the integration of machine learning techniques into process design and analysis. Since 2020, he has focused on the potential of ionic liquids for gas storage and separation, aiming to provide innovative pathways to address industrial energy and environmental challenges. Alongside this, he is developing advanced quantitative structure–property relationship (QSPR) models to predict the behavior of ionic liquids and guide their design for targeted applications. By combining fundamental thermodynamics with data-driven approaches, his work seeks to enhance efficiency, sustainability, and innovation in chemical process industries. His long-term goal is to contribute to the development of computationally enabled solutions that advance cleaner and more sustainable chemical technologies.
Affiliations and expertise
Amirkabir University of Technology, Tehran, IranAH
Abdolhossein Hemmati-Sarapardeh
Abdolhossein Hemmati-Sarapardeh is currently an associate professor at Shahid Bahonar University of Kerman. He is also an adjunct professor at Jilin University and Northeast Petroleum University in China. He was previously a visiting scholar at the University of Calgary. He earned a PhD in petroleum engineering from Amirkabir University of Technology, an MSc in hydrocarbon reservoir engineering from the Sharif University of Technology, and a BSc in petroleum engineering from the Amirkabir University of Technology. His research interests include enhanced oil recovery processes, heavy oil systems, nanotechnology, and applications of intelligent models in the petroleum industry. Abdolhossein has been awarded as a distinguished graduate MSc student, was an honor PhD student, and a recipient of the National Elites Foundation Scholarship. He works as an associate professor in the Journal of Petroleum Science and Engineering. He has published over 150 journal articles, three books, several conference proceedings, and earned one patent in 2016.
Affiliations and expertise
Assistant Professor, Department of Petroleum Engineering, Shahid Bahonar University of Kerman, IranAM
Ahmad Mohaddespour
Dr. Ahmad Mohaddespour obtained his PhD in Chemical Engineering from McGill University in 2013, specializing in polymers and nanocomposites. From 2013 to 2015, he completed a postdoctoral fellowship at Polytechnique Montréal, where he collaborated with industrial partners such as TOTAL and OCP on projects involving supercapacitors, mineral processing, and sensor data analysis. Between 2015 and 2017, he pursued a second postdoctoral fellowship at the University of British Columbia, where he worked on carbon capture technologies in partnership with DOMTAR and explored the application of artificial intelligence to chemical engineering problems. Since 2017, his research has focused on porous materials for energy storage and carbon capture, as well as AI-driven approaches to predict the properties of chemicals and complex compounds. By combining materials science, computational modeling, and process engineering, his work aims to deliver innovative solutions for energy and environmental sustainability.
Affiliations and expertise
Fellow, McGill University, Montreal, Canada