While the relevant features and properties of nanosystems necessarily depend on nanoscopic details, their performance resides in the macroscopic world. To rationally develop and accurately predict performance of these systems we must tackle problems where multiple length and time scales are coupled. Rather than forcing a single modeling approach to predict an event it was not designed for, a new paradigm must be employed: multiscale modeling.
A brilliant solution to a pervasive problem, Multiscale Modeling: From Atoms to Devices offers a number of approaches for which more than one scale is explicitly considered. It provides several alternatives, from coarse-graining sampling of the atomic and mesoscale to Monte Carlo- and thermodynamic-based models that allow sampling of increasingly large scales up to multiscale models able to describe entire devices.
Beginning with common techniques for coarse-graining, the book discusses their theoretical background, advantages, and limitations. It examines the application-dependent parameterization characteristics of coarse-graining along with the finer-trains-coarser multiscale approach and describes three carefully selected examples in which the parameterization, although based on the same principles, depends on the actual application.
The book considers the use of ab initio and density functional theory to obtain parameters needed for larger scale models, the alternative use of density functional theory parameters in a Monte Carlo method, and the use of ab initio and density functional theory as the atomistic technique underlying the calculation of thermodynamics properties of alloy phase stability.
Highlighting one of the most challenging tasks for multiscale modelers, Multiscale Modeling: From Atoms to Devices also presents modeling for nanocomposite materials using the embedded fiber finite element method (EFFEM). It emphasizes an ensemble Monte Carlo method to high field-charge transport problems and demonstrates the practical application of modern many-body quantum theories.
The author maintains a website with additional information.
About the Author: Pedro Derosa is affiliated with Louisiana Tech University and Grambling State University in Ruston, Louisiana.
