Multiscale Dynamics Simulations: Nano and Nano-bio Systems in Complex Environments

Over the past decade, significant progress has been made in developing methodologies to treat complex nano- and nano-bio systems embedded in complex environments. Multiscale Dynamics Simulations covers methods such as DFT/MM-MD, DFTB, and semi-empirical QM/MM-MD, as well as machine-learning approaches, providing newcomers with a comprehensive menu of multiscale modeling options.

Format: Hardback
Length: 388 pages
Publication date: 01 October 2021
Publisher: Royal Society of Chemistry

Over the past decade, remarkable advancements have been made in developing methodologies capable of effectively treating increasingly complex nano- and nano-bio systems, intricately embedded within diverse environments. Multiscale Dynamics Simulations serves as a comprehensive resource that encompasses a range of methods, including DFT/MM-MD, DFTB, and semi-empirical QM/MM-MD, DFT/MMPOL, as well as machine-learning approaches, to address these challenges. By delving into key methodological breakthroughs, this book offers newcomers a comprehensive menu of multiscale modelling options, empowering them to navigate the intricate landscape of the nano/bio world with greater clarity and proficiency.

The field of multiscale dynamics simulations has witnessed significant progress in recent years, driven by the need to understand and manipulate complex nano- and nano-bio systems that are embedded in diverse environments. This multidisciplinary field encompasses a wide range of methods, including density functional theory (DFT), molecular dynamics (MD), and machine learning (ML), among others. One of the key challenges in multiscale dynamics simulations is the accurate representation of the interactions between atoms and molecules at different length scales. This requires the development of sophisticated computational techniques that can handle the complexity of these systems.

One of the most widely used methods in multiscale dynamics simulations is DFT/MM-MD, which combines DFT calculations with MD simulations to study the dynamics and properties of systems at the atomic and molecular levels. This method allows for the simulation of large systems with millions of atoms, making it possible to study complex biological processes such as protein folding, drug discovery, and membrane dynamics. Another important method in multiscale dynamics simulations is DFTB, which combines DFT calculations with Born-Oppenheimer molecular dynamics simulations to study the electronic structure and dynamics of systems. This method is particularly useful for studying systems with heavy atoms, such as metals and semiconductors, which are important in various applications such as electronics, energy, and materials science.

In addition to these methods, semi-empirical QM/MM-MD and DFT/MMPOL are also used in multiscale dynamics simulations. Semi-empirical QM/MM-MD combines quantum mechanics calculations with molecular mechanics simulations to study the electronic structure and dynamics of systems. This method is particularly useful for studying systems with large molecules or complex structures, as it can provide a more accurate description of the electronic interactions than purely quantum mechanical methods. DFT/MMPOL, on the other hand, combines DFT calculations with Monte Carlo simulations to study the thermodynamic and kinetic properties of systems. This method is particularly useful for studying systems with a large number of degrees of freedom, such as fluids and gases, which are important in various applications such as astrophysics, chemistry, and engineering.

Machine learning is also being increasingly used in multiscale dynamics simulations to improve the accuracy and efficiency of the modelling process. Machine learning algorithms can be trained to learn the patterns and relationships in the data and use this knowledge to make predictions about the behaviour of systems. This has the potential to revolutionize the field of multiscale dynamics simulations, as it can allow for the prediction of complex phenomena that are difficult to model using traditional methods.

One of the key applications of multiscale dynamics simulations is in drug discovery. By simulating the interactions between drugs and their target molecules, researchers can identify potential drug candidates and predict their efficacy and safety. This has the potential to reduce the time and cost of drug development and improve the success rate of new drugs.

In conclusion, multiscale dynamics simulations have made significant progress in recent years, enabling researchers to understand and manipulate complex nano- and nano-bio systems with greater accuracy and efficiency. The development of sophisticated computational techniques, such as DFT/MM-MD, DFTB, semi-empirical QM/MM-MD, DFT/MMPOL, and machine learning, has opened up new avenues for research and innovation in the field. As the demand for more complex systems continues to grow, it is likely that multiscale dynamics simulations will play an increasingly important role in advancing our understanding of the world and developing new technologies.

Weight: 773g
Dimension: 234 x 156 (mm)
ISBN-13: 9781839161780