The prospect of modeling across disparate length and time scales to achieve a predictive multiscale description of real materials properties has attracted widespread research interest in the last decade. To be sure, the challenges in such multiscale modeling are many, and in demanding cases, such as mechanical properties or dynamic phase transitions, multiple bridges extending from the atomic level all the way to the continuum level must be built. Although often overlooked in this process, one of the most fundamental and important problems in multiscale modeling is that of bridging the gap between first-principles quantum mechanics, from which true predictive power for real materials emanates, and the large-scale atomistic simulation of thousands or millions of atoms, which is usually essential to describe the complex atomic processes that link to higher length and time scales. For example, to model single-crystal plasticity at micron length scales via dislocation-dynamics simulations that evolve the detailed dislocation microstructure requires accurate large-scale atomistic information on the mobility and interaction of individual dislocations. Similarly, modeling the kinetics of structural phase transitions requires linking accurate large-scale atomistic information on nucleation processes with higher length and time scale growth processes.

Networks have recently emerged as a unifying theme in complex systems research [1]. It is in fact no coincidence that networks and complexity are so heavily intertwined. Any future definition of a complex system should reflect the fact that such systems consist of many mutually interacting components. These components are far from being identical as say electrons in systems studied by condensed matter physics. In a truly complex system each of them has a unique identity allowing one to separate it from the others. The very first question one may ask about such a system is which other components a given component interacts with? This information system wide can be visualized as a graph, whose nodes correspond to individual components of the complex system in question and edges to their mutual interactions. Such a network can be thought of as a backbone of the complex system. Of course, system's dynamics depends not only on the topology of an underlying network but also on the exact form of interaction of components with each other, which can be very different in various complex systems. However, the underlying network may contain clues about the basic design principles and/or evolutionary history of the complex …

The principal quantum mechanical theories of the mind/brain connection are described.