Integrated Nanoscale Experimentation, Prediction, and Validation

Summary

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:

• Improve knowledge through integrated experimentation and prediction
• Evaluate and improve model validity through careful comparison to appropriate reference values

Description

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.

Major Accomplishments

• Defect Structures and Electronic Properties of Graphene—Density Functional Theory simulations of defect structures and electronic properties of graphene for direct comparison with scanning tunneling microscope measurements of electronic properties
• Development of atomic-scale models for determination of local structure of nanostructures from combined X-ray, electron, and neutron diffraction techniques
• Concurrent Experiments and Simulations of Zinc Oxide Nanowires—Molecular Dynamics simulations of structure and properties of zinc oxide nanowires for direct comparison with atomic force microscope measurements of elastic properties
• Validation of Classical Atomistic Simulations of Nanoscale Material Phenomena—A classical atomistic simulation of a nanoscale material phenomena is the amalgamation of no fewer than three independent models: (1) the interatomic potential, (2) the nanostructure atomistic configuration, and (3) the procedure by which the property is computed. This activity applies the goals of the Materials Genome Initiative each independent model.
• Electron Backscatter Diffraction measurements of structure and strain of barium titanate for direct comparison with continuum models of strain— Orientation, stress, and strain in an (001) barium titanate single crystal with 90° lamellar domains determined using electron backscatter diffraction
• Strain Mapping and Simulation—Finite Element simulations of deformation, strain, and stress states of silicon and silicon-germanium thin-film structures for direct comparison with electron backscatter diffraction, X-ray diffraction, confocal Raman microscopy, and atomic force microscopy measurements of deformation, strain, and stress.
• Finite element and analytical mechanics models of buckled metallic lines on low dielectric constant thin films for direct comparison with atomic force microscopy measurements of buckling geometry and material properties
• Mapping of Strain Fields and Phase Distributions of Indented Si using Raman-Spectroscopy Enhanced Instrumented Indentation Technique (IIT)—In Situ Raman spectroscopy measurements of strain states in indented silicon for direct comparison with atomic-scale predictions of Raman shift with strain
• Machine Learning for High Throughput Materials Discovery and Optimization Applications—Development of search algorithms to mine large experimental data sets to identify phase and other cluster diagrams with a focus on electronic materials