The goal of this collaborative research is to build a computational framework with experimental validation to systematically engineer designer materials that provide targeted properties. Defects in materials significantly influence their properties, for instance in energy transfer. The current state-of-the-art techniques allow one to induce defects in materials controllably and subsequently predict and correlate relevant properties to the defect concentration. In this research, computational and experimental approaches will be integrated to construct designer defect-engineered materials that will provide desired properties. In this approach, first a targeted property will be ascertained, and then the defect concentration and distribution will be predicted to synthesize novel material structures that provide the predetermined property. The hybrid computational framework will motivate the general scientific community to leverage advanced computing infrastructures. The efforts will establish new research and learning communities for computational-experimental data-enabled science and engineering through promotion of diversity and undergraduate research experience, outreach to community college students and work-force development, outreach to the general public (through education about the power of simulation-based engineering with an online gaming tool and hands-on activities at science centers) and integration of research with education.
This project is a comprehensive effort to rigorously integrate developments in high throughput synthesis and characterization with improving access and availability to high performance computing resources towards solving an inverse design of materials problem. A computational framework that sweeps through millions of possible structural permutations and combinations for property prediction will be developed and validated against experiments. The framework will (1) employ massively parallel molecular dynamics simulations to predict transport properties of nanomaterials, (2) synthesize defect engineered nanostructures and measure the corresponding transport properties, (3) integrate the above nanoscale computations and experiments through a genetic algorithm based hybrid optimization scheme to predict the optimal material structure for specified transport properties and formulate a hierarchy of increasingly complex duals of cost-functionals and design parameters, and finally (4) close the loop by validating the inverse design results experimentally.
