Quantum computing provides the potential for polynomial to exponential speed-up of computations for applications in materials science, machine learning, and cryptography. We are currently applying quantum algorithms to advance materials computational research. This is focused two types of quantum computing approachs: traditional gate operations and quantum annealing. With respect to quantum gate systems, we are evaluating implementation of quantum linear algebra algorithms on superconducting qubit hardware for fidelity, scalability, and error analysis for future engineering applications that use the finite element and finite difference methods. An example of recent developments is shown on the right which has been implemented on IBM's quantum computing hardware. With respect to quantum annealing, we are developing algorithms for D-Wave hardware for use in un-supervised and supervised neural networks applications. This is being applied to materials design problems for fuel cells applications.

Azobenzene is a fascinating material that undergoes photoisomerization (light induced molecular changes).  This behavior strongly depends on the wavelength and polarization of light.  When synthesized in a polymer, cooperative liquid crystal microstructure (and amorphous materials) motion induced by light leads to a macroscopic shape change of the polymer. 

We are exploring the complex photomechanical behavior of these unique materials to improve their efficiency for next generation engineering adaptive structures.  This includes a combination of molecular to continuum scale light-matter modeling, photomechanical experiments, and time-resolved solid state nuclear magnetic resonance (NMR).  Recent modeling results have identified how a model of dipole forces can be used to predict homogeneous film bending, Gaussian beam structures for linear and circularly polarized light, and optical vortex induced polymer film textures.  This avoids the need for higher order electronic structure variables (electric field gradients) and mass diffusion models to predict a wider variety of deformation modes from complex light sources.

Current research is focused on advanced chemistry that utilizes triplet states to minimize the energy necessary to induce trans-cis behavior in photopolymers. This has been demonstrated using a combination of photoresponsive molecules and sensitizer molecules with select triplet energy states. Significant enhancements in photostrictive stress has been achieved by testing the photochemical reaction within a polyimide glassy polymer.

Network theory is currently being applied to develop an atomistic material network to advance computational efficiencies and understand complex inter-atomic interactions. As part of this research, we are using spectral sparsification to reduce the size of the network which is originally defined by a complete graph of interatomic forces. The goal of spectral sparsification is to strategically reduce the number of network edges (interatomic forces) without producing significant errors, but substantially increase computational efficiency. We are comparing spectral sparsification to conventional thresholding (radial cut-off distance) of molecular forces for different atomistics potentials. Two potentials considered thus far include the Lennard-Jones potential and the Coulomb potential. The spectral sparsification for the Lennard-Jones potential yields comparable results while spectral sparsification of the Coulomb potential outperforms the thresholding approach. The results show promising opportunities which may accelerate molecular simulations containing long-range electrical interactions which are relevant to a broad range of materials with strong dipole or magnetic coupling.

We are studying the relations between fractional constitutive properties of material and their fractal structure structure. Elastomer foams have been the focus of this research which exibit complex viscoelasticity that is difficult to predict across a broad range of deformation rates and finite deformation. In the images on the right, we have applied the fractional time derivative within a nonlinear continuum model that accommodates hyperelasticity and fractional viscoelasticity within an entropy generation function. The modeling has been validated experimentally by calibrating the model at one stretch rate and extrapolating the model to predict viscoelasticity at other stretch rates. This prediction is shown on the right where fractional rates clearly provide better prediction than the integer order model. The 95% PI and 95% CI represents the 95% prediction interval and 95% credible intervals computed using Bayesian statistics.

Current research is focused on relating the observed viscoelastic predictions to the underlying structure of this elastomer. The elastomer on the left is a foam material made by 3M (VHB 4910). The fractional order was ~0.15 for this material. Similar predictions have been successfully achieved on VHB 4949 where the fractional order is ~0.65. We are utilizing microscopy to quantify the fractal structure of these materials and other foam materials to understand how to design foams with desired hyperelastic, viscoelastic, and piezoresistive material properties.

Legged robots provide unique mobility and maneuverability over wheeled robotic platforms. Over complex terrains, legged robots provide extraordinary advantages over wheeled robots due to dynamic mobility characteristics; however, transitions from one terrain to another often requires alterations of the internal stiffness and damping of the structures contained within the robotic appendages. To address this issue, we are integrating electroactive polymers into the legs of robots. We have shown up to 100% stiffness change on the order of 0.1 sec (commensurate with the stride of the iSPRAWL (hexapod) robot pictured).

The key enabling material used in this system is the dielectricelastomer VHB.  Constitutive models have been developed that incorporate finite deforming membrane electromechanics and uncertainty quantification.  Model validation of the nonlinear viscoelastic behavior can be found here.  The code and data required to run this Bayesian uncertainty analysis is found here.