Networking Behavior in Thin Film and Nanostructure Growth Dynamics

Thin film coatings have been essential in development of several micro and nano-scale devices. To realize thin film coatings various deposition techniques are employed, each yielding surface morphologies with different characteristics of interest. Therefore, understanding and control of the surface growth is of great interest. In this paper, we devise a novel network-based modeling of the growth dynamics of such thin films and nano-structures. We specifically map dynamic steps taking place during the growth to components (e.g., nodes, links) of a corresponding network. We present initial results showing that this network-based modeling approach to the growth dynamics can simplify our understanding of the fundamental physical dynamics such as shadowing and re-emission effects

Thermal-Expansion and Fracture Toughness Properties ofParts made from Liquid Crystal Stereolithography Resins

Liquid crystal (LC) resins are a new kind ofstereolithography material that can produce parts with structured or ordered morphologies instead ofthe amorphous morphologies that result from standard resins. The LC molecules can be aligned before cure resulting in an anisotropic crosslinked network when the laser induced polymerization "locks-in" the alignment. Previous papers have explored liquid crystal orientation dynamics [1], the effects of orientation on viscoelastic and mechanical properties [2,3], and the processing ofLC resins by stereolithography [4]. This paper considers the effects ofmorphology on fracture toughness and thermal-expansion properties. Both toughness and thermal-stability continue to be important issues for stereolithography parts. The use ofLC resins may provide a way to significantly improve performance in both ofthese areas, and in addition result in parts with high upper use . temperatures.Mechanical Engineerin

Interpretable deep learning for guided structure-property explorations in photovoltaics

The performance of an organic photovoltaic device is intricately connected to its active layer morphology. This connection between the active layer and device performance is very expensive to evaluate, either experimentally or computationally. Hence, designing morphologies to achieve higher performances is non-trivial and often intractable. To solve this, we first introduce a deep convolutional neural network (CNN) architecture that can serve as a fast and robust surrogate for the complex structure-property map. Several tests were performed to gain trust in this trained model. Then, we utilize this fast framework to perform robust microstructural design to enhance device performance.Comment: Workshop on Machine Learning for Molecules and Materials (MLMM), Neural Information Processing Systems (NeurIPS) 2018, Montreal, Canad

Scalable Co-Optimization of Morphology and Control in Embodied Machines

Evolution sculpts both the body plans and nervous systems of agents together over time. In contrast, in AI and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge. In psychology, the theory of embodied cognition posits that behavior arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioral performance. Here, we further examine this hypothesis and demonstrate a technique for "morphological innovation protection", which temporarily reduces selection pressure on recently morphologically-changed individuals, thus enabling evolution some time to "readapt" to the new morphology with subsequent control policy mutations. We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioral training -- while simultaneously providing a testbed to investigate the theory of embodied cognition

Viscoelastic Properties of Dynamically Asymmetric Binary Fluids Under Shear Flow

We study theoretically the viscoelastic properties of sheared binary fluids that have strong dynamical asymmetry between the two components. The dynamical asymmetry arises due to asymmetry between the viscoelastic stresses, particularly the bulk stress. Our calculations are based on the two-fluid model that incorporates the asymmetric stress distribution. We simulate the phase separation process under an externally imposed shear and compare the asymmetric case with the usual phase separation under a shear flow without viscoelastic effects. We also simulate the behavior of phase separated stable morphologies under applied shear and compute the stress relaxation.Comment: 10 pages text, 9 figure

Impact of global structure on diffusive exploration of organelle networks

We investigate diffusive search on planar networks, motivated by tubular organelle networks in cell biology that contain molecules searching for reaction partners and binding sites. Exact calculation of the diffusive mean first-passage time on a spatial network is used to characterize the typical search time as a function of network connectivity. We find that global structural properties --- the total edge length and number of loops --- are sufficient to largely determine network exploration times for a variety of both synthetic planar networks and organelle morphologies extracted from living cells. For synthetic networks on a lattice, we predict the search time dependence on these global structural parameters by connecting with percolation theory, providing a bridge from irregular real-world networks to a simpler physical model. The dependence of search time on global network structural properties suggests that network architecture can be designed for efficient search without controlling the precise arrangement of connections. Specifically, increasing the number of loops substantially decreases search times, pointing to a potential physical mechanism for regulating reaction rates within organelle network structures.Comment: 13 pages, 4 figures. Accepted for publication in Scientific Report