Introduction to Computational Electrochemistry
Modelling Methods and Applications in Interfacial Phenomena, Electrocatalysis, and Energy Storage
- 1st Edition - March 1, 2027
- Latest edition
- Editors: Hyungjun Kim, Stefan Ringe, Leanne D. Chen
- Language: English
Introduction to Computational Electrochemistry: Modelling Methods and Applications in Interfacial Phenomena, Electrocatalysis, and Energy Storage addresses the various method… Read more
Description
Description
Introduction to Computational Electrochemistry: Modelling Methods and Applications in Interfacial Phenomena, Electrocatalysis, and Energy Storage addresses the various methodologies and intricate processes involved in electrochemical energy interconversion. Recent advancements in incorporating both the electronic responses of electrodes and the molecular dynamic responses of electrolytes are highlighted, thus enabling a deeper understanding of the physicochemical processes occurring at electrode-electrolyte interfaces. The book also introduces applications of modern computational chemistry to various electrochemical systems, including electrocatalytic systems for efficient energy conversion and energy storage systems such as batteries and supercapacitors. Emphasis is placed on state-of-the-art multiscale approaches for the advanced simulation of electrochemical interfaces.
By presenting case studies that illustrate underlying mechanisms, explaining experimental observations, and guiding the design of improved systems, the book shows how computational electrochemistry increasingly interplays with experiments in the field of electrochemistry. This book aims to help pave the way for near-future developments that will unravel the atomic details of electrochemical interfaces and foster the growth of non-conventional methodological approaches.
By presenting case studies that illustrate underlying mechanisms, explaining experimental observations, and guiding the design of improved systems, the book shows how computational electrochemistry increasingly interplays with experiments in the field of electrochemistry. This book aims to help pave the way for near-future developments that will unravel the atomic details of electrochemical interfaces and foster the growth of non-conventional methodological approaches.
Key features
Key features
- Provides a multidisciplinary resource that includes access to the field from various perspectives, unifying ideas and concepts
- Covers quantum chemistry, describing chemical reactions, surface science to study processes at catalyst surfaces, materials science to optimize catalysts, and chemical engineering to optimize mass transport and multi-scale factors
- Introduces recent advances in modeling electrochemical interfaces as electrochemical systems have recently gained significant attention due to the growing importance of renewable energy technologies
- Helps students gain understanding of the field by mixing cutting-edge research developments in both methods and applications in an educational context to create an up-to-date introduction to computational electrochemistry
Readership
Readership
Invaluable resource for computational and experimentally oriented researchers working in the field of electrochemistry, particularly those addressing complex interfaces at various scales; It is especially suitable for supporting the education of graduate students, with a focus on both methodological development and high-level, challenging applications; It is particularly aimed at graduate students and post-doctoral fellows, helping them familiarize themselves with the latest advances in simulation methods for modeling complex electrochemical interfaces; It also explores various applications of computational modeling in understanding and designing electrochemical materials
Table of contents
Table of contents
Part I: Fundamentals in Computational Electrochemistry
Editor Prologue: Overview of Current Developments and Challenges in Methods and Models
Section A: Quantum Chemical Modeling of Electrochemical Interfaces
1. Electrochemical Potential and Its Representation in Quantum Chemical Modeling
2. Electrochemical Capacitance and Its Representation in Quantum Chemical Modeling
Section B: Surrogate Atomistic Models of Electrochemical Interfaces
3. Electric Double Layer Structure, Capacitance, and Phase Transitions from Hybrid Quantum-Classical Simulations
4. Electric Double Layer: From Quantum Chemical to Classical Depictions
5. Machine-Learning for Next-Generation Computational Electrochemistry
6. The Importance of Potentiostats for Correctly Replicating Electrochemical Conditions
Section C: Continuum Modeling of Electrochemical Interfaces
7. Next-Generation Continuum Solvation Models for Modeling Electrochemical Interfaces
8. Mastering the Use of Continuum Solvation Methods for Modeling Electrochemistry
9. Hybrid Density-Functional Theoretical Models of Electric Double Layers
Section D: Kinetic and Multi-Scale Modeling of Electrochemical Processes
10. Theoretical Foundations Behind First-Principles Electrochemical Barriers
11. Multi-Scale Modeling for Electrochemical Energy Conversion
Part II: Computational Electrocatalysis
Editor Prologue: Advances in Electrocatalysis Driven by Computational Simulations
Section A: Electrocatalyst Design in the Static Equilibrium Limit
12. Computational Design of Catalysts for Oxygen Evolution Reaction
13. Microenvironment Effects in Catalysis
14. A Systematic Approach for Modelling Disordered Surfaces
15. Nanomaterials and Active Site Engineering for Electrocatalysis
16. Toward Data‐and Mechanistic‐Driven Volcano Plots in Electrocatalysis
17. Towards a Computational Hydrogen Electrode 2.0: References in Electrochemistry
Section B: Insights into Electrocatalysis from Ab Initio Molecular Dynamics
18. Insights into Electrochemical CO2 Reduction from Ab Initio Molecular Dynamics
19. Insights into Oxygen Reduction Reaction Kinetics from Ab Initio Molecular Dynamics
Section C: First Principles-Driven Kinetic and Multi-Scale Modeling of Electrocatalytic Processes
20. Nonadiabatic Proton-Coupled Electron Transfer at Surfaces
21. Towards Affordable First-Principles Electrochemical Barriers
22. Deciphering Electrocatalytic Processes from First-Principles, Continuum Modeling, and Multi-Scale Simulations
Part III: Computational Modeling of Energy Storage
Editor Prologue: Next Generation Energy Storage Systems Enabled by Computational Modeling
Section A: Energy Storage Modeling in the Static Equilibrium Limit
23. First-Principles Insights into Energy Storage of MXenes
24. Combining Theory and Experiments for Insights into Lithium-Ion Batteries
Section B: Dynamics and Kinetics of Energy Storage Systems
25. Computational Design of Battery Electrolytes
26. Ion and Electron Transport in Electrochemical Energy Storage Devices and Materials
27. Hybrid Quantum-Classical Simulations of MOF Capacitors
Section C: Data-Driven Energy Storage System Design
28. Applying Machine Learning Methods to Electrode Materials for Li-Ion Batteries
29. Machine Learning and Multiscale Modelling in Materials Design
30. A Data-Driven Approach to Materials Design and Discovery
Part IV: Summary and Perspectives
31. Conclusion
Editor Prologue: Overview of Current Developments and Challenges in Methods and Models
Section A: Quantum Chemical Modeling of Electrochemical Interfaces
1. Electrochemical Potential and Its Representation in Quantum Chemical Modeling
2. Electrochemical Capacitance and Its Representation in Quantum Chemical Modeling
Section B: Surrogate Atomistic Models of Electrochemical Interfaces
3. Electric Double Layer Structure, Capacitance, and Phase Transitions from Hybrid Quantum-Classical Simulations
4. Electric Double Layer: From Quantum Chemical to Classical Depictions
5. Machine-Learning for Next-Generation Computational Electrochemistry
6. The Importance of Potentiostats for Correctly Replicating Electrochemical Conditions
Section C: Continuum Modeling of Electrochemical Interfaces
7. Next-Generation Continuum Solvation Models for Modeling Electrochemical Interfaces
8. Mastering the Use of Continuum Solvation Methods for Modeling Electrochemistry
9. Hybrid Density-Functional Theoretical Models of Electric Double Layers
Section D: Kinetic and Multi-Scale Modeling of Electrochemical Processes
10. Theoretical Foundations Behind First-Principles Electrochemical Barriers
11. Multi-Scale Modeling for Electrochemical Energy Conversion
Part II: Computational Electrocatalysis
Editor Prologue: Advances in Electrocatalysis Driven by Computational Simulations
Section A: Electrocatalyst Design in the Static Equilibrium Limit
12. Computational Design of Catalysts for Oxygen Evolution Reaction
13. Microenvironment Effects in Catalysis
14. A Systematic Approach for Modelling Disordered Surfaces
15. Nanomaterials and Active Site Engineering for Electrocatalysis
16. Toward Data‐and Mechanistic‐Driven Volcano Plots in Electrocatalysis
17. Towards a Computational Hydrogen Electrode 2.0: References in Electrochemistry
Section B: Insights into Electrocatalysis from Ab Initio Molecular Dynamics
18. Insights into Electrochemical CO2 Reduction from Ab Initio Molecular Dynamics
19. Insights into Oxygen Reduction Reaction Kinetics from Ab Initio Molecular Dynamics
Section C: First Principles-Driven Kinetic and Multi-Scale Modeling of Electrocatalytic Processes
20. Nonadiabatic Proton-Coupled Electron Transfer at Surfaces
21. Towards Affordable First-Principles Electrochemical Barriers
22. Deciphering Electrocatalytic Processes from First-Principles, Continuum Modeling, and Multi-Scale Simulations
Part III: Computational Modeling of Energy Storage
Editor Prologue: Next Generation Energy Storage Systems Enabled by Computational Modeling
Section A: Energy Storage Modeling in the Static Equilibrium Limit
23. First-Principles Insights into Energy Storage of MXenes
24. Combining Theory and Experiments for Insights into Lithium-Ion Batteries
Section B: Dynamics and Kinetics of Energy Storage Systems
25. Computational Design of Battery Electrolytes
26. Ion and Electron Transport in Electrochemical Energy Storage Devices and Materials
27. Hybrid Quantum-Classical Simulations of MOF Capacitors
Section C: Data-Driven Energy Storage System Design
28. Applying Machine Learning Methods to Electrode Materials for Li-Ion Batteries
29. Machine Learning and Multiscale Modelling in Materials Design
30. A Data-Driven Approach to Materials Design and Discovery
Part IV: Summary and Perspectives
31. Conclusion
Product details
Product details
- Edition: 1
- Latest edition
- Published: March 1, 2027
- Language: English
About the editors
About the editors
HK
Hyungjun Kim
Hyungjun Kim is a Professor at the Department Chemistry, Korean Advanced Institute of Science and Technology (KAIST), Republic of Korea. He obtained his Ph.D. in Chemistry in 2009 from Caltech. After three and a half years in a senior researcher position at KAIST, he started his faculty position at KAIST in 2013. He is an author of more than 220 peer-reviewed journal papers, and now also a junior member of the Korean Academy of Science and Technology. His main research interest is in developing new computational methods for material simulations and electrochemical interfaces.
Affiliations and expertise
Full Professor, Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Republic of KoreaSR
Stefan Ringe
Stefan Ringe is an Associate Professor at the Department of Chemistry, Korea University, Republic of Korea. He obtained his Ph.D. in Theoretical Chemistry from the Technical University of Munich in 2017. After Postdoctoral research stays at Stanford University, USA and KAIST, he became an Assistant Professor at DGIST (Daegu, Rep. of Korea), from where he transferred to Korea University in 2022. His research interest focusses on computational electrochemistry in all its challenges, from the simulation and optimization of materials, electrolytes and their interfaces to multi-scale modelling of realistic devices. He is an author of more than 40 peer-reviewed journal papers with his milestone papers focussing on electrochemical CO2 reduction.
Affiliations and expertise
Associate Professor, Department of Chemistry, Korea University, Republic of KoreaLC
Leanne D. Chen
Leanne D. Chen is an Associate Professor at the University of Guelph, Canada. She received her PhD from Stanford University in 2017, took up a two-year postdoctoral position at Caltech until 2019, then started her independent career in 2020. She currently leads a creative and collaborative group with a common goal of using quantum chemistry methods to gain fundamental insight and reduce our reliance on fossil fuels for energy applications. As an early-career researcher, she has secured more than half a million CAD in funding and has given 39 invited talks around the world. Her contributions to the field of computational electrocatalysis are evidenced by a series of contributions in high-impact venues including Journal of the American Chemical Society, ACS Catalysis, and Nature Communications, with a total of 38 publications.
Affiliations and expertise
Associate Professor, Electrochemical Technology Centre, Department of Chemistry, University of Guelph, Canada