"Enhancing the performance limits of nano-structured materials through atomistic modeling, experimental validation and design optimization"

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It is often remarked that the road to success is paved with failure. This is particularly true for materials technology; understanding how materials fail paves the way to make them better. Despite significant breakthroughs in recent decades through nanotechnology, advanced materials typically fail at one-tenth or less of their intrinsic limits, and we do not understand why this is. This failure of advanced structural materials is a principal bottleneck for developing future technologies in a wide range of industrial sectors such as energy, healthcare and aerospace. By itself failure modeling is an age old problem - the novelty comes from the accuracy of details you put in the mathematical model that describes failure. In the past, modeling material failure has been ad-hoc and empirical. The material scientist's dream is to put in basic details of the material into a model and then predict its failure precisely without resorting to ad-hoc parameters. This dream is starting to become achievable through a judicious coordination of computational materials science and experimental validation. This project aims to take empiricism out of failure modeling and make clear-cut progress towards computational materials design. Our focus will be on failure modeling in novel nano-structured material systems using computational materials science. State of the art atomistic modeling techniques will be employed to study failure in a variety of materials systems: nanocrystalline microtruss hybrid materials, nanocomposites and nano-engineered alloys. The current research program aims to achieve two major goals: (1) Develop and experimentally validate computational models that can accurately predict properties and failure of these materials without resorting to empirical parameters, and (2) Suggest routes for improving material performance limits through modification at the nanometer level. The applicant's hope is to develop novel material designs by efficiently combining atomistic failure modeling with experimental validation and design optimization.