Methods in Molecular Modelling: Methods, Algorithms and Implementation: Methods in Molecular and Materials Modelling
Editat de David O. Scanlonen Limba Engleză Paperback – 30 apr 2025
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.
- Introduces the basics of each method in a confident manner, similar to the very successful TYC modelling course
- Provides an overview of what can be done using current methods on current computer architectures
- Includes balanced explanations of the limitations of certain approaches for certain problems will help beginners become confident in what method to use
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Specificații
ISBN-13: 9780443187636
ISBN-10: 0443187630
Pagini: 350
Dimensiuni: 151 x 229 mm
Editura: ELSEVIER SCIENCE
Seria Methods in Molecular and Materials Modelling
ISBN-10: 0443187630
Pagini: 350
Dimensiuni: 151 x 229 mm
Editura: ELSEVIER SCIENCE
Seria Methods in Molecular and Materials Modelling
Public țintă
Researchers and students new to materials modelling and interested in its application in modern materials chemistry, materials physics and materials scienceCuprins
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