Introduction To Methods In Materials Modelling: Methods, Algorithms And Implementation

Erscheinungsjahr 2027
ISBN: 978-0-443-18763-6
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 152 mm x 229 mm, Gewicht: 450 g

Methods in Molecular Modelling: Methods, Algorithms and Implementation offers a brief and concise overview of methods available for materials modeling, enabling readers to identify the right level of theory for their calculations. The book explains each method, providing an understanding on which method and level of theory to apply to get trusted results for minimum computational costs. It serves as the foundational volume for a series that explores different application areas in detail. The theoretical foundations of methods for modeling molecules and materials are complex, and in addition to physics and chemistry, require a solid understanding of mathematics and programming.

Beginners in this area will appreciate helpful case studies and examples of applications in this book rather than the derivation of complicated equations. Through a balanced ratio of theoretical background of methods and practical application of these methods to representative problems from physics, chemistry, and materials science, this book teaches how to perform calculations that enable them to design new materials. More experienced researchers will also appreciate the practical applications described that can be used to prepare lectures.

Autoren/Hrsg.

Scanlon, David O.

Ganose, Alexander

Fachgebiete

• Naturwissenschaften Chemie Physikalische Chemie

Inhaltsverzeichnis

Part 1 Fundamentals of Materials Modelling1. Interatomic Potentials2. Introduction to DFT and Periodic DFT3. Dealing with the Self Interaction Error4. Molecular Dynamics and AIMD5. Linear Scaling DFT6. GW Theory7. QM/MM Techniques in Materials Modelling8. Quantum Monte CarloPart 2 Practicalities of Materials Modelling4. Molecular Calculations5. Surface Calculations6. Vibrational Properties7. Modelling of nano-clusters and particles8. Crystal Structure Prediction9. Machine Learning in DFT and IP Modelling10. Modelling Nucleation11. Modelling defects in solids12. Treating Van der Waals In DFT13. Modelling Porous Materials14. Conclusion and Future Outlook

Über Autor(innen)

Scanlon, David O.
David O. Scanlon is Chair in Computational Chemistry in the School of Chemistry, University of Birmingham, UK where he leads the Scanlon Materials Theory Group (SMTG). David gained his BA.(Mod) Computational Chemistry in 2006 and PhD in Chemistry in 2011 from Trinity College Dublin, where he carried out his research under the supervision of Professor Graeme W. Watson. In 2011 he moved to University College London (UCL), UK to take up a Ramsay Fellowship in the Department of Chemistry, hosted by Professor Sir Richard Catlow, FRS. In September of 2013 he was appointed to a Lectureship in the Department of Chemistry at UCL, a joint appointment with Diamond Light Source, and was promoted to Reader in 2016 and Professor in 2018. In September 2023, he joined the University of Birmingham.