AI for Materials Design
The AI for Materials Design Laboratory is a platform for AI-driven materials design using high-throughput experimentation. The team includes expertise in robotics, AI, high-rate experimentation, high-throughput characterization, and batch fabrication and processing. The current focus of the laboratory is on high-entropy alloys. The Graham-Brady research group develops machine-learning models that support real-time decision making in the AI-driven design framework.
Machine learning for Mechanics of Materials
Machine learning provides a host of incredibly powerful tools for efficient representations of material microstructure and mechanical behavior. Such representations enable probabilistic sampling and multi-scale modeling of materials at unprecedented structural scales; however, the uncertainty associated with these models remains an open research question. The Graham-Brady research group has looked at applications in simulation of two-phase and polycrystalline microstructures and in multi-scale modeling of composite materials, as well as uncertainty quantification of machine learning pipelines.
Surrogate Modeling and Uncertainty Quantification
Uncertainty quantification relies on the ability to draw representative samples from complex computational models of interest. In order to avoid a large number of full model evaluations, surrogate models provide an efficient approximation to the relationship between the input parameters and the output response. The Graham-Brady research group uses both adaptive sampling and machine learning to build these surrogate models. More information here
Simulation of Material Microstructure
Digital generation of microstructure provides an efficient means of performing parametric studies on materials with random microstructure, without resorting to extensive characterization of physical specimens. These tools also enable generation of three-dimensional microstructures consistent with two-dimensional observations. Stochastic simulation and machine learning serve as the basis for these simulations. The Graham-Brady research group has looked at applications in polycrystalline materials, multi-phase composites and functionally graded materials. More information here
Stochastic Homogenization
Traditional homogenization considers the effective mechanical properties of a representative volume element (RVE), which assumes that the relevant material behavior is averaged over the macro-scale. However, many failure mechanisms are associated with more localized behavior, requiring that homogenization be performed at an intermediate meso-scale, which means that the material properties vary randomly. For 20 years, the Graham-Brady research group has been applying stochastic homogenization as a way to understand stress localizations and failure. More information here
Dynamic Failure of Ceramics
Dynamic failure mechanisms in ceramics are fundamentally different than those in static failure. While static failure is dictated by a small number of large pre-existing defects, dynamic failure is governed by crack growth from a large number of multi-sized defects. Coalescence of this large number of cracks leads to finer fragmentation of the material, such that the material transforms into a granular medium. The Graham-Brady research group models the mechanisms of brittle dynamic failure in ceramics, including damage growth, fragmentation and granular flow of the fragmented material. More information here
Probabilistic Mechanics and Stochastic Finite Elements
All real materials exhibit random variations that affect structural reliability. Probabilistic mechanics tools such as stochastic finite elements provide a means of mapping probabilistic descriptors of materials to probabilistic descriptors of structural behavior. The Graham-Brady research group develops techniques for characterizing the uncertainty in material microstructure, in material properties and in identifying the variability of mechanics models. More information here
Modeling of Cementitious Materials
Because of their inherent random inhomogeneities and brittle failure, models of cementitious materials present a significant modeling challenge. The Graham-Brady group has looked at two primary areas associated with these materials: 1) damage growth and fragmentation at high rates, and 2) localizations of stress and/or damage due to random inhomogeneous microstructure. More information here