The increased performance of modern computational systems has enabled the size and time scales of materials models to be increased significantly. In parallel, the increased precision of modern experimental systems has enabled the size scale of measurements of properties of materials to be decreased significantly. At this nexus, the vision of the Materials Genome Initiative can be realized by integrating these efforts. This page provides an overview of activities, which seek to:
A central tenet of the Materials Genome Initiative is that advanced computational methods will enable materials with new or superior properties to be discovered so as to improve the performance of components in applications ranging from communications to energy to health care. Such methods will take advantage of the reduced cost, increased speed, and increased data handling ability of modern computational hardware and software to implement models of materials, and in particular to generate models of material structures that can be interrogated to predict material properties. The success of such a scheme relies on the validity of the computational models. The goal of these projects is to provide validation of computational models of materials properties by direct comparison of predicted and measured properties from multiple sources, such as density functional theory, classical atomistic simulation, scanning probe microscopy, etc.
The increased speed and increased size of datasets implemented in modern computational systems has enabled the size scale of models of materials to be increased significantly. For example, molecular dynamics simulations can be performed using many millions of virtual atoms to describe and predict the properties of structures many tens of nanometers in scale. In parallel, the increased precision of modern experimental systems has enabled the size scale of measurements of properties of materials to be decreased significantly. For example, scanning probe microscope-based measurements can be performed to measure and map mechanical, thermal, and electrical properties with sub-nanometer resolution. Hence, at the nanoscale it is now possible to make direct comparison of experimentally determined and computationally predicted properties for the same structure. Examples of such structures include nanoparticles with unique chemical properties for biochemical applications, thin films with superior mechanical properties for nanoelectromechanical sensor and actuator applications, and lithographically-defined structures with novel quantum mechanical properties for nanoelectronic applications.
Central to materials discovery and model validation is the deliberate co-evolution of the experimental measurements and computational predictions to advance the Material Genome Initiative goals. For example, an experimental measurement might reveal a variation in a property with the dimensions or composition of a nanostructure or material. Modeling and simulation could then be used to provide guidance on the underlying cause of the material phenomena. If the agreement between experiment and prediction is very good, new structures or compositions could then be rapidly predicted to search for materials with new or enhanced properties for new applications. A critical element to this approach is the deliberate a priori selection of material systems and structures that are amenable to both experimental and computational investigation in a direct and absolute manner. This last point is an essential part of the co-evolution: Both the experimental measurement and simulated prediction must also include an assessment of accuracy such that absolute values of properties can be predicted, not simply trends.