| Poster Number |
Title | Author(s) | Department(s) | Abstract |
| 1 | Examination of Nonlinearities in the Van Allen Probes Data During Geomagnetic Storms |
Lois Keller Smith, Michael Liemohn |
AOSS | In the recently released Van Allen Probes data, signatures indicating geomagnetic activity were examined closely. In particular, we chose several different storms throughout 2012-2013 where a large ionized oxygen flux was seen in conjunction with a significant drop in the disturbed storm time (DST) index. For these storms, the electric and magnetic fields in a cartesian coordinate system were analyzed for coupled nonlinearities. Preliminary results show that during storm time, the magnetic field in the z direction will drop dramatically, sometimes reversing direction in extreme situations. The results of this work give a clearer pictures of the behavior of the inner magnetosphere during storm times. |
| 3 | Integrating Gaze Tracking into Virtual Reality for Assessing User Experience |
Rachael Havens, Eric Maslowski, Theodore Hall |
Electrical Engineering and Computer Science |
With the advancement of technology, immersive human-computer interaction (HCI) is quickly becoming vital for modernized computer systems—as seen with the rise in popularity of virtual reality rooms and tile displays. These systems, for all their state-of-the-art technology, are still limited in how well they create a sense of immersion for the user. Gaze tracking, if integrated into these systems, can greatly augment user immersion and data analysis, and, in doing so, open doors for HCI. Once recorded, the user’s point of gaze or pupil diameter could be utilized to add a new dimension to data collection. Additionally, real-time gaze tracking could improve the motion capture system, allowing the virtual environment to react to where the user is looking rather than just where the user is facing. This project’s aim is to develop and incorporate an inexpensive gaze tracking system into the Jugular software system. This can be used for desktops/tiled displays, and fully immersive virtual reality systems such as the Michigan Immersive Digital Experience Nexus (MIDEN) to enhance future users’ immersive experience and gather new insight for research purposes. The Sony PS Eye camera was modified into an infrared camera. This was accomplished through low-cost, easy to acquire materials, designing a 3D model of the lens mount, and rapid prototyping technology. This IR camera will be mounted onto the MIDEN’s 3D glasses and used to actively track the user’s gaze. The 2-dimensional coordinates will be adjusted to 3-dimensional with respect to the motion capture data. |
| 4 | Discovery and Design of New Materials via Computer Simulations |
Pablo F. Damasceno, Michael Engel, Carolyn Phillips, Sharon Glotzer |
Applied Physics |
The process of Self-Assembly – in which the components of a system organize themselves into an ordered pattern – represents an exciting route for the design of nano and colloidal materials. Currently, there is no theory able to predict the structure that will emerge from a system of locally interacting agents and researchers aiming to isolate the different factors contributing to this process have to rely on computer simulations and simplified models. Our work has focused in understanding the role of some essential factors for self-assembly (such as shape, Entropy and Enthalpy) as well as using them to discover, predict and design new target patterns. The utilization of cutting-edge computer hardware and software, allied to the best visualization and rendering techniques we have developed, have been crucial to the progress of this research that can revolutionize materials and technologies to come. |
| 5 | Basic Social Science on the Web |
Jon Atwell | Sociology/Center for the Study of Complex Systems |
The emergence of new sources of socially-oriented data and the democratization of research computing power has introduced new questions and methods to the social sciences. Other than leading to exciting basic research, these changes offer the opportunity to engage undergraduate students in new and promising ways. This project does so through a series of labs in which students gather and analyze these new data using basic statistics, sentiment analysis, and network analysis. The labs are built around IPython Notebooks in order to significantly reduce the skills necessary for students to get and explore these data while also leaving robust functionality at their finger tips. A trial run of the labs in an undergraduate seminar has already been hugely successful. This poster will describe the labs in more detail, present example results and discuss its effectiveness in the classroom. |
| 6 | Mathematical and Epidemiological Modeling of the Pathogenesis of Human Papilloma Virus (HPV) in Oropharyngeal Squamous Cell Carcinoma (OSCC) |
Pritika Dasgupta, Rafael Meza, Thomas Carey, Marisa Eisenberg |
Epidemiology | OBJECTIVE: Although cervical and other genital cancers are primarily caused by HPV infections, recent studies have demonstrated that many individuals also carry HPV in their oral cavity, and a significant fraction of Head and Neck Cancers (HNC) contain HPV-DNA, suggesting that the virus may be the underlying root of these cancers. Indeed, 90% of University of Michigan (UM) oropharyngeal cancer patients and 50% of nasopharyngeal cancer patients carry high-risk HPV. Recent research suggests that HPV is an etiologic agent for a rapidly growing subset of head and neck squamous cell carcinoma (HNSCC), particularly oropharyngeal squamous cell carcinomas (OSCC). There remain many open questions about the potential mechanisms by which HPV induces HNC, as well as the connection between the rising oral HPV infection and oropharyngeal cancer incidence. Thus, in order to learn more about the potential interactions between HPV and HNC, the objectives of this study are to develop dynamic systems biology models of regulatory networks affected by HPV and pathways associated with OSCC malignancy and assess the consequences of HPV infection on the dynamics of tumor cell proliferation through the use of a sexual behavior and networks questionnaire. METHODS: The available genomic, expression, and physiological data used to develop a mathematical model of oral HPV natural history of infection was found through a literature search on the HPV genome, biology of the oropharynx, and HNC. The gene regulatory networks were mathematically represented with ordinary differential equations (ODEs), neglecting spatial effects with mRNA and protein concentrations as continuous time variables. The modeling software used to analyze and fit these ODEs was Python. RESULTS: A simple and complex model of the mechanism and transmission of HPV infection and OSCC carcinogenesis and their corresponding parameters were proposed and organized from a literature search. Parameter estimation and fitting of the simple model have produced preliminary results on HPV infection dynamics, which validates our methods for further analysis. The draft of the sexual behavior and networks questionnaire is still being revised. CONCLUSIONS: Further analysis, parameter estimation, parameter fitting, development of genotyping protocols, and further modeling of the early and late stages of HPV infection in OSCC are paramount. Moreover, this continuing research will also utilize information from a sexual behavior and networks questionnaire, to be piloted in January 2014. The data simulated and collected from the completion of the two primary aims will result in an overall HPV-transmission network model with individuals placed in different OSCC risk groups with different HPV transmission rates, based on sexual behavior. |
| 7 | Vibrational Energy Relaxation of Cyanide Ion In Aqueous Solution |
Surma Talapatra, Eitan Geva |
Chemistry | Vibrational energy relaxation (VER) of molecules in condensed phase provides direct information about the intermolecular and intramolecular energy exchange phenomena. For systems like cyanide ion in aqueous solution, the strong forces generated by the interactions between the ionic species and the polar solvent molecules, coupled with the quantum effects arising from the high vibrational energy of the solute (~2080 cm-1), result in an enhancement of the rate of vibrational relaxation. An in-depth computational analysis is essential in order to develop a molecular level understanding of the fast relaxation processes observed in systems like these. We have calculated the VER rate constant for three isotopomers of cyanide ion (12C14N-, 12C15N-, 13C15N-) in H2O and D2O at 300K using Landau Teller formula where the quantum-mechanical bath force autocorrelation function is calculated using a linearized semiclassical (LSC) approximation. The calculations are repeated using classical molecular dynamics simulations, wherein the quantum effect on the relaxation is accounted for by using quantum correction factors (QCFs). The calculated VER rate constants have excellent quantitative agreement with the results obtained in previous experimental studies. Furthermore, using two solvent models we have elucidated the plausible relaxation pathways: (1) A pathway involving the translational and rotational modes of water as accepting modes (studied using a rigid water model), (2) A pathway involving the bending modes of water as accepting modes (studied using a flexible water model). Our results confirm the experimentally observed trend of relaxation rate constants for the range of cyanide ion isotopomers. We find that although classical mechanics predicts that pathway 2 is the dominant one, the stronger quantum enhancement of the VER rate in pathway 1 compensates for it, thereby making both pathways viable. |
| 8 | A Computational Approach to Rational Design for Organic Optoelectronic Devices |
Heidi Phillips, Shaohui Zheng, Zilong Zheng, Francis Devine, Eitan Geva, Barry Dunietz |
Chemistry | A fundamental understanding of charge transfer processes and charge separation is necessary for efficient development and design of organic optoelectronic devices. Computational approaches provide fundamental insights to these processes at the atomistic level, and serve as cost-effective avenues to guide the synthesis of novel optoelectronic materials. Conventional density functional theory (DFT) methods, such as LDA, GGA, hybrid functionals, have been known to fail in accurately characterizing frontier orbital gaps and charge transfer states in molecular systems. In order to address these failures, we implement the BNL (Baer-Neuhauser-Livshits) range-separated hybrid (RSH) functional approach within DFT and time-dependent DFT. The RSH approach was benchmarked using model systems and then applied to complex silsesquioxane molecules, which are currently investigated as candidates for photovoltaic applications. A protocol utilizing charge-constrained DFT and the polarizable continuum solvent model was implemented to account for the effect of complex environments on charge transfer state energies. The protocol was tested and validated by comparing the charge transfer state energies to experimental measurements on silsesquioxane molecules. The RSH approach was also applied to promising molecular systems for pure-organic, phosphorescent light emitting diode materials, including dibenzophoshole chalcogenide and bromobenzaldehyde derivatives. |
| 9 | A Treecode-Accelerated Boundary Integral Poisson-Boltzmann Solver for Electrostatics of Solvated Proteins |
Weihua Geng, Robert Krasny |
Mathematics | We present a treecode-accelerated boundary integral (TABI) solver for electrostatics of solvated proteins described by the linear Poisson-Boltzmann equation. The method employs a well-conditioned boundary integral formulation for the electrostatic potential and its normal derivative on the molecular surface. The surface is triangulated and the integral equations are discretized by centroid collocation. The linear system is solved by GMRES iteration and the matrix-vector product is carried out by a Cartesian treecode which reduces the cost from O(N^2) to O(NlogN), where N is the number of faces in the surface triangulation. The TABI solver is applied to compute the electrostatic solvation energy of PDB:1A63 (2069 atoms, RNA binding domain of E. coli rho factor). We present the error, CPU time, and memory usage, and compare results for the Poisson-Boltzmann and Poisson equations. We compare TABI results with those obtained using the grid-based APBS code, and we also present parallel TABI simulations using up to eight processors. The TABI solver exhibits good serial and parallel performance combined with relatively simple implementation and efficient memory usage. |
| 10 | High-Fidelity Aerodynamic Shape Optimization of a Blended-Wing-Body Aircraft |
Peter Zhoujie Lyu, Joquim R.R.A. Martins |
Aerospace | A series of aerodynamic shape optimization studies for a blended-wing-body aircraft are performed using Reynolds-averaged Navier–Stokes computational fluid dynamics with a Spalart–Allmaras turbulence model. A gradient-based optimization algorithm is used in conjunction with a discrete adjoint method that computes the derivatives of the flow equations including the linearization of the turbulence model. A total of 273 shape and planform design variables are optimized. The objective function is the drag coefficient at the nominal cruise condition. Lift, trim, center plane bending moment, and static margin constraints are considered. The study investigates the impact of the optimization problem formulation on optimized blended-wing-body designs. Control surfaces at the rear centerbody are used to trim the aircraft via a nested free-form deformation volume approach. Trim and static stability are investigated under both on- and off-design conditions. A comparison of single-point and multi-point optimization is also presented. |
| 11 | Simulation of Quantum Circuits via Stabilizer Frames |
Hector J. Garcia, Igor L. Markov |
Computer Science Engineering |
Generic quantum-circuit simulation appears intractable for conventional computers and may be unnecessary because useful quantum circuits exhibit significant structure that can be exploited during simulation. For example, Gottesman and Knill identified an important subclass, called stabilizer circuits, which can be simulated efficiently using using group-theory techniques and insights from quantum physics. Realistic circuits enriched with quantum error-correcting codes and fault-tolerant procedures are dominated by stabilizer subcircuits and contain a relatively small number of non-stabilizer components. Therefore, we develop new data structures and algorithms that facilitate parallel simulation of such circuits. Stabilizer frames offer more compact storage than previous approaches but require more sophisticated bookkeeping. Our implementation, called Quipu, simulates certain quantum arithmetic circuits (e.g., reversible ripple-carry adders) in polynomial time and space for equal superpositions of n-qubits. On such instances, known linear-algebraic simulation techniques, such as the (state-of-the-art) BDD-based simulator QuIDDPro, take exponential time. We simulate quantum Fourier transform and quantum fault-tolerant circuits using Quipu, and the results demonstrate that our stabilizer-based technique empirically outperforms QuIDDPro in all cases. While previous high-performance, structure-aware simulations of quantum circuits were difficult to parallellize, we demonstrate that Quipu can be parallelized with a nontrivial computational speedup. |
| 12 | CHARACTERIZATION OF THE TEMPERATURE DISTRIBUTION OF GALAXY CLUSTERS SIMULATED WITH SPH AND AMR CODES |
ELENA RASIA, ERWIN LAU, STEFANO BORGANI, DAISUKE NAGAI, KLAUS DOLAG, CAMILLE AVESTRUZ, GIANLUIGI GRANATO, PASQUALE MAZZOTTA, GIUSEPPE MURANTE, CINTHIA RAGONE-FIGUEROA |
Physics | A clarification on the theoretical predictions of the bias on the hydrostatic equilibrium mass is increasingly urgent after the claimed discrepancy on the cosmological parameters derived by the cosmic-microwave background and cluster counts made by the Planck team. The literature built on synthetic cluster catalogues suggests bias values ranging from 10% to 30%, with the largest number justified by the presence of an additional temperature bias caused by thermal fluctuations of the medium. In this poster, we make an important step forward in understanding the different predictions of mass bias by evaluating the degree of temperature in-homogeneity generated in both smooth-particle-hydrodynamics (SPH) code and adaptive-mesh-refinement (AMR) one. On average, mesh-code are more efficient in homogenizing the medium, especially in the center and in case of no-radiative physics. In the most external regions, we expect a temperature bias twice as large in SPH simulations with respect to AMR runs. Once compared with data obtained from the central regions of ∼ 60 clusters, the level of temperature structures in AMR are underestimated by about 20% and are within 10% in SPH for objects with central temperature greater than 5 keV. The inclusion of AGN in SPH simulations improves the agreement to 5% while the introduction of thermal conduction apparently produces an independence of the temperature dispersion from the cluster tempera- ture. Constrained on physical mechanisms influencing the cluster thermal structure will be possible by enlarging the observed sample of clusters to either higher temperatures or larger radii. |
| 13 | Process Geometric Constraint Mining by Feature-based Analysis |
Yuqing Zhou, Kazuhiro Saitou |
Mechanical Engineering |
With the increasingly growing demand of lightweight and better crashworthiness performance on automotive body structural design, the designers nowadays are more likely to encounter new materials, or even new manufacturing processes they are not familiar with. The design guidelines for those new materials as well as processes will be of great value for designers to improve the manufacturability/assemblability of the components. Instead of going through years of trial-and-error practice in manufacturing plant, this poster proposed a knowledge based process geometric constraint modeling synthesis through process simulation, design of experiment and data mining. The extracted process constraints on part geometry are both interpretable by designers and easy to be embedded into structural optimization algorithms. |
| 15 | An FMM-FFT Accelerated Hybrid Volume Surface Integral Equation Solver for Electromagnetic Analysis of Plasma-Engulfed Vehicles |
Abdulkadir C. Yucel, Luis J. Gomez, Yang Liu, Hakan Bagci, Eric Michielssen |
Electrical Engineering and Computer Science |
Space vehicles often are affected by communication blackout upon re-entering the Earth’s atmosphere. The blackout arises when the vehicle interacts with the atmosphere around it, giving rise to dense plasmas that are impenetrable by electromagnetic waves. The vehicle itself often is covered in a thin and inhomogeneous plasma shell, the density of which decreases rapidly with distance from the vehicle surface. This plasma shell hinders the operation of antennas mounted on the side of the vehicle. As the vehicle moves through space, it also leaves behind a large plasma plume. This plume hinders the operation of antennas mounted on the back of the vehicle. The nature and density of the plasma shell and wake heavily depend on operational and environmental conditions and vary rapidly with the vehicle’s position along its trajectory. To analyze the occurrence of communication blackout and facilitate the design of robust navigation systems, fast simulators capable of accurately characterizing the operation of antennas mounted on plasma-engulfed vehicles are called for. The majority of past efforts aimed at analyzing antennas in plasma environments have relied on finite difference time domain solvers and were limited to relatively simple antennas and platforms [1]. Unfortunately they do not permit modeling of antennas on realistic structures. Recently a ray tracing technique was used to analyze antennas on plasma engulfed re-entry vehicles [2]. Albeit very powerful, this approach does not allow for a detailed modeling of the antennas and/or complex plasma structures that may arise in a turbulent wake. In this study, a hybrid full-wave simulator that addresses the aforementioned challenges in analyzing scattering and radiation from plasma-engulfed space vehicles is proposed. The hybrid full-wave solver uses a surface integral equation solver to model the currents on the perfect electric conducting surface of the vehicle and a volume integral equation solver to model the electromagnetic fields in the plasma surrounding the vehicle. Both solvers were accelerated by a fast Fourier transform and fast multipole method (FMM-FFT) [3]. The hybrid solver uses a special block diagonal preconditioner for efficient and accurate characterization of scattering and radiation from plasma-engulfed space vehicles in highly inhomogeneous plasma distributions. Various computational results that demonstrate the efficiency, accuracy, and modeling versatility of this hybrid full-wave solver will be presented. |
| 16 | Sources of bias in measures of allele-specific expression derived from RNA-seq data aligned to a single reference genome |
Kraig Stevenson, Joseph Coolon, Patricia Wittkopp |
Department of Computational Medicine and Bioinformatics |
RNA-seq can be used to measure allele-specific expression (ASE) by assigning sequence reads to individual alleles; however, relative ASE is systematically biased when sequence reads are aligned to a single reference genome. Aligning sequence reads to both parental genomes can eliminate this bias, but this approach is not always practical, especially for non-model organisms. To improve accuracy of ASE measured using a single reference genome, we identified properties of differentiating sites responsible for biased measures of relative ASE. We found that clusters of differentiating sites prevented sequence reads from an alternate allele from aligning to the reference genome, causing a bias in relative ASE favoring the reference allele. This bias increased with greater sequence divergence between alleles. Increasing the number of mismatches allowed when aligning sequence reads to the reference genome and restricting analysis to genomic regions with fewer differentiating sites than the number of mismatches allowed almost completely eliminated this systematic bias. Accuracy of allelic abundance was increased further by excluding differentiating sites within sequence reads that could not be aligned uniquely within the genome (imperfect mappability) and reads that overlapped one or more insertions or deletions (indels) between alleles. After aligning sequence reads to a single reference genome, excluding differentiating sites with at least as many neighboring differentiating sites as the number of mismatches allowed, imperfect mappability, and/or an indel(s) nearby resulted in measures of allelic abundance comparable to those derived from aligning sequence reads to both parental genomes. |
| 17 | Scoring Protein Interactions using CRAPome – a Contaminant Repository for Affinity Purification Mass Spectrometry Data |
Dattatreya Mellacheruvu, Zachary Wright, Anne-Claude Gingras, Alexey Nesvizhskii |
DCMB | ffinity purification coupled with mass spectrometry (AP-MS) is widely used for the identification of protein-protein interactions. However, in a given experiment, distinguishing between true interactions and background contaminants (e.g. proteins interacting with the solid-phase support, affinity reagent or epitope tag) is a challenging task. While the standard approach is to use negative controls to identify background contaminants, most small-scale AP-MS studies fail to comprehensibly characterize the background. Negative controls, however, are bait-independent and aggregating data from multiple AP-MS studies can increase the coverage. Hence, we developed a curated and annotated repository of negative controls called the Contaminant Repository for Affinity Purification Mass Spectrometry data (CRAPome). We present here the repository and methods to score protein interactions using the CRAPome. |
| 18 | Uncovering pH-dependent Excited States in Proteins using pH-based Replica Exchange Constant pH Molecular Dynamics Simulations and GPU Acceleration |
Garrett B. Goh, Laricheva, E. N., and Charles L. Brooks III |
Chemistry | pH plays a crucial role in regulating biological activity such as protein folding, enzyme catalysis and protein-protein interactions, and understanding the protein’s dynamical response to pH changes will improve our fundamental understanding of such processes. Recent advances have demonstrated the increasing importance of transiently populated minor states in protein dynamics and function, but due to their low populations, pH-dependent excited states are difficult to characterize experimentally. Here, we report on the development and application of a novel computational method, an explicit solvent constant pH molecular dynamics (CPHMD) framework to model realistic pH-coupled dynamics in proteins. In CPHMD, the protonation states of residues are dynamically changing in response to the external pH environment and conformational dynamics of the protein. To improve the convergence of our simulations, we utilize a pH-based replica exchange sampling method, which is a loosely coupled and highly parallelizable algorithm. In addition, we utilized new GPU technology that is capable of running molecular dynamics simulations orders of magnitude faster than traditional CPU hardware to improve the sampling of our simulations. To probe the atomistic-level details of pH-dependent excited states, CPHMD simulations was used to interrogate the dynamics of a series of staphylococcal nuclease (SNase) mutants with buried ionizable residues at different pH environments. Amongst the key findings from our simulations is the existence of open state structure for these “buried” residues, where local solvation around the protonating site was observed in all SNase mutants with highly shifted pKa values. In the most extreme examples of pKa shifts recorded by experiments, these open state structures are transiently populated at physiological pH, contributing a small but non-zero population of 1-2%. Nevertheless, sampling these open states was discovered to be a necessary condition for accurately reproducing experimental pKa measurements, to which our calculated pKa values demonstrated good agreement with experiments with a low average unsigned error of 1.3 pKa units and correlation coefficient (R2) of 0.78. The work we present here provides the first validation that buried ionizable residues can readily evoke pH-triggered conformational fluctuations that are propagated into physiological pH range as excited states. Furthermore, the good agreement between calculated and experimental pKa values also demonstrate that explicit solvent CPHMD simulations are able to model pH-coupled dynamics in both hydrophilic and hydrophobic environments, paving the way for future applications of such as modeling pH-dependent protein folding and biological activity of membrane proteins. |
| 19 | Accelerating the Discrete Element Method using GPUs in HOOMD-Blue |
Matthew Spellings, Ryan Marson, Sharon Glotzer |
Chemical Engineering |
Although faceted shapes are commonly created in experimental systems of colloidal and nanoscale particles, many interesting physical phenomena, such as crystalline nucleation and growth and glassy dynamics, are challenging to model in these systems. A variety of computational models have been developed over the years, but each has its own set of advantages and disadvantages. The Discrete Element Method (DEM) is used extensively to study granular |
| 20 | The Seasonal Migration of H3N2 |
Daniel Zinder, Manojit Roy, Edward B. Baskerville, Mercedes Pascual |
DCMB | Influenza A (H3N2) is circulating in the human population since 1968 and continues to maintain considerable morbidity and mortality. Genetic sequences sampled since its introduction serve for combined evolutionary and epidemiological inference. Despite it being highly seasonal, the role of seasonality in determining global migration and persistence patterns has not been directly established, in lack of an adequate computational model, and due to confounding factors such as air-travel and demographics. Using a Bayesian Metropolis-Coupled MCMC framework, we explore the seasonal migration of H3N2, integrating in parallel over a massive number of phylogenetic tree topologies and model configurations; using open source software developed in our lab and the University of Michigan’s HPC system. We initially focus on China and North-America and later on multiple global communities, showing key windows in time, in which successful invasion takes place. We attribute H3N2 migration seasonality to high incidence and growth periods at the source community and matching epidemic troughs at the destination. Quantifying and improving our understanding of H3N2 migration, will further our understanding of flu ecology, expand the horizon of prediction, and eventually lead to better matching vaccines; identifying candidate strains that are not only able to escape immunity but also have the right seasonal opportunity to establish themselves. |
| 21 | A Well-Conditioned Volume-Surface Field Integral Equation (VSCFIE) for Inhomogeneous Cylindrical Scatterers with High-Electrical Contrasts |
Luis J. Gomez*, and Eric Michielssen |
EECS | Volume integral equations (VIEs) are commonly used to analyze scattering from inhomogeneous dielectric objects. Unfortunately, when applied to high-contrast objects, their discretization calls for very fine meshes and results in ill-conditioned systems of equations. This severely limits the utility of VIEs in modeling electromagnetic phenomena germane to a host of biological and geophysical applications. A partial solution to this problem was proposed by Usner and coworkers, Here, we propose a method that alleviates both these problems. We start from a subdivision of the object into |
| 22 | Tuning the HOMO-LUMO Gap in Conjugated Polymers for Organic Photovoltaics Applications based on First-Principles Calculations |
Xiao Ma, Hossein Hashemi, Bonggi Kim, Jinsang Kim, John Kieffer |
Material science and engineering |
To tune the HOMO and LUMO energy levels via alternating donor-acceptor monomer units, we investigated a series of conjugated polymers (CP)s in which the electron withdrawing power of the acceptor group and the electron giving power of the donor group is varied, while maintaining the same conjugated chain conformation. We observed that the introduction of electron withdrawing groups lowers the LUMO level, while keeping the HOMO level almost unchanged. Conversely, inserting the electron donating groups raises the HOMO level while maintaining the LUMO level unchanged. According to these trends, designing a low band gap polymer requires strong donors and acceptors. Using first-principles calculations we investigated underlying reason. Charge localization on the electron-rich and electron-poor segments in CPs plays a key role. We identified strong correlations between the withdrawing strength of the acceptor group, the HOMO and LUMO levels, and the degree of orbital localization, which allows us to derive reliable design principles for CPs. |
| 23 | Employing Machine Learning for Fast, Accurate, and Easily Accessible Predictions of Activation Energies |
Jordan N. Metz*, Paul M. Zimmerman |
Chemistry | Fast and reliable predictions of the favorable products formed from given reactant species at set conditions are of great interest to aid in the design of synthetic procedures. Approaches towards obtaining this insight have utilized a number of techniques including quantum chemical transition state search methods, rule-based expert systems, and machine learning. Although, these rule-based expert systems and machine learning techniques bypass the expense of quantum chemical transition state search methods, the currently proposed models suffer several drawbacks including complexity and difficulties in the transfer to additional reaction types among others. In our work, we demonstrate a simple machine learning technique that directly predicts the activation energy using common quantum properties of reactant-product species capable of providing insight across a variety of reaction types. Initial pericyclic studies reveal activation energies within ~2kcal/mol of quantum chemical calculations on average where the prediction timescale occurs on the order of milliseconds. |
| 24 | Python Enabled Atomistic Simulation and Analysis of Highly Cross-linked Epoxy Networks |
Katherine Sebeck, Michael J. Waters, John Kieffer |
Materials Science and Engineering |
Highly cross-linked glassy polymers such as epoxy are widely used in industrial adhesive and polymer matrix composite applications. The mechanical properties of these structures are highly dependent on the topological nature of the network. Prediction of the mechanical properties of these materials is vital for efficient design. However, this fundamental topology of the network is difficult to probe experimentally. A series of epoxy structures have been generated using a combination of molecular dynamics simulations in LAMMPS and a Python-enabled dynamic polymerization code. We examine the effect of amine functionality and system size on the behavior of these network structures. |
| 25 | Kirke: A Python toolkit for molecular simulations. |
M. J. Waters, K. Sebeck, E. J. Coyle, and J. Kieffer |
Materials Science and Engineering |
Molecular simulation is a field rife with different file formats and bespoke analysis methods. Even with standard simulation codes such as LAMMPS, VASP, AMBER, etc., there are many one-time-use codes written for pre/post-processing of molecular structures. We sought to reduce redundant coding labor in our simulation group by creating a shared library for the reading, writing, manipulation, and analysis or molecular simulation data. By using a common data structure for our molecular structures, all of these collaboratively written functions can be used as agnostically as possible of input or output format. Using this system, we have saved significant intellectual overhead simply through elimination of redundancy. The library is written in Python 2.x with some functional dependence on NumPy, SciPy, and Matplotlib. |
| 26 | High-Throughput Screening for Carbon Capture and Chemical Energy Storage Materials |
Hyun Seung Koh, Malay Kuma Rana, Jacob Goldsmith, Antek Wong-Foy, Michael Cafarella, Donald J. Siegel |
Mechanical Engineering |
Metal-organic frameworks (MOFs) have recently emerged as promising materials for carbon capture and methane storage. Even if there are over thousands of MOFs were synthesized, and proposed over 100,000 MOFs, there are lack of knowledge about gas adsorption. Thus, we identify MOFs structures from Cambridge Structural Database, and clean the structures that we find. We calculate H2 uptake of 22,800 cleaned MOFs by Chahine rules, and discover there is a limitation increasing both volumetric and gravimetric uptake. Also, Grand Canonical Monte Carlo (GCMC) results show the same relation is in CH4 simulations, and we found some untested good MOFs for H2, and CH4 storage. Amongst these many possible MOFs, metal- substituted compounds based on M-DOBDC and M-HKUST-1 have demonstrated amongst the highest capacities for CO2 and CH4 at moderate pressures and temperatures. Here we also explore the possibility for additional performance tuning by computationally screening several metal-substituted variants of these compounds with respect to their CO2 adsorption enthalpies and CH4 capacities. In the case of CO2, our screening identifies 13 compounds having adsorption enthalpies within the targeted thermodynamic window -40 to -75 kJ/mol. Also, the partial charge on the coordinatively unsaturated metal sites is found to correlate with the adsorption enthalpy, suggesting that this property may be used as a simple performance descriptor for carbon capture efficiency. For methane storage, calculated adsorption enthalpies are found to be 10-20 kJ/mol less exothermic than for CO2, consistent with a weaker, dispersion-based CH4—MOF interaction. In parallel with these thermodynamic analyses, methane adsorption isotherms were predicted using GCMC simulations across the remainder of the M-DOBDC series, with additional comparisons to prominent MOFs. PCN-11 & Be-DOBDC yield the best combination of usable gravimetric and volumetric methane densities at pressures below ~50 bar, while MOF-5 is best at higher pressures. |
| 27 | Self-assembly of colloidal complementary shape alloys |
Eric S. Harper, Ryan L. Marson, Joshua A. Anderson, Sharon C. Glotzer |
Materials Science and Engineering |
Self-assembly of nanoparticles and colloids holds great promise to create useful new materials. Nearly ubiquitous in nature, self-assembly is a powerful tool for organizing matter, observed in everything from the formation of virus capsids and protein folding to the bubbles on top of a glass of beer. Scientists and engineers in a wide variety of disciplines hope to better understand how different particles self-assemble to facilitate the engineering of desired assemblies of materials. While the use of DNA tethers, chemical functionalization, and even magnets have been shown to effectively direct self-assembly, the effect of particle shape on self-assembly has received surprisingly little attention. Nature makes extensive use of shape-based complementary interactions, such as the “lock and key” mechanism of enzymes and protein recognition at the surface of a cell. Utilizing both MPI-accelerated and GPU-accelerated Metropolis Monte Carlo simulations, we investigate a class of complementary “shape alloys” that can be used to stabilize desired phases. These complementary particles fit together in a manner similar to that of puzzle pieces. We investigate two dimensional systems of polygons: squares, pentagons, and hexagons. These shapes are split into halves as well as complementary halves e.g. squares into rectangles and right isosceles triangles. By using our visualization and analysis software, we see evidence that this shape complementarity promotes the desired assembly, even in a system which otherwise would not assemble e.g. the right isosceles triangles. |
| 28 | Hot carbon corona in Mars’ upeer thermosphere and exosphere: solar cycle and seasonal variability |
Yuni Lee, Michael R. Combi, Valeriy Tenishev, Stephen W. Bougher |
AOSS | This work presents the variability over seasons (i.e., orbital position) and solar cycle of the Martian upper atmosphere and hot carbon corona. We investigate the production and distribution of energetic carbon atoms and the impacts on the total global hot carbon loss from all dominant photochemical processes at five different cases: AL (aphelion and solar low), EL (equinox and solar low), EH (equinox and solar high), PL (perihelion and solar low), and PH (perihelion and solar high). We compare our results with published results, but on only limited cases because of the dearth of studies on solar EUV flux and orbital position variations. Photodissociation of CO and dissociative recombination of CO+ are generally regarded as the two most important source reactions for hot atomic carbon. Of these two, photodissociation of CO is found to be the dominant source in all cases considered. To describe self-consistently the exosphere and the upper thermosphere, a combination of our 3D Direct Simulation Monte Carlo (DSMC) model and the 3D Mars Thermosphere General Circulation Model (MTGCM) is used. The basic description of this hot carbon calculation can be found in the companion paper to this one (Lee et al. 2013a). The spatial distributions and profiles of density and temperature and atmospheric loss rates are discussed for the cases considered. |
| 29 | Computational Simulation of Seismic Collapse Capacity of Steel Columns |
Julie Fogarty, Sherif El-Tawil |
Civil & Environmental Engineering |
The effect of local buckling due to lateral loading on the axial capacity of steel columns is investigated using detailed finite element models at the member, subassemblage, and system levels. An overview of past research addressing column failure under seismic loading is presented to characterize the current state of knowledge. Preliminary simulation results indicate that flange local buckling due to lateral loading could significantly reduce the axial resistance of steel moment frame columns. of steel moment frame columns. |
| 31 | Cluster Stacking: Increasing Precision and Accuracy in Galaxy Cluster Mass Estimation |
Nicholas Kern, Daniel Gifford, Christopher Miller |
Astronomy | Galaxy clusters, regions in the universe where galaxies are found to be clustered together, are the largest gravitationally bound structures in the universe, and play a crucial role in the study of the evolution of the universe. By measuring the total (dark matter) mass of these clusters we can analyze how clusters form and can even constrain fundamental parameters of the evolution of the universe. However, measuring the total mass of galaxy clusters accurately and precisely is one of the major barriers in observational cosmology. The caustic technique is one competing method, which uses the newtonian kinematics, positions and velocities, of the clustered galaxies to estimate the mass of the underlying dark matter content (Gifford et al. 2013). However, the precision and accuracy of the caustic technique suffers greatly from low number statistics. We show that by taking multiple clusters with low number statistics, and stacking their phase space together to create an ensemble cluster, we can greatly reduce the intrinsic bias and scatter, and obtain precise and accurate mass estimates of clusters with sample sizes as low as N ~ 5. We use N-body simulation data to analyze the statistical improvements the stacking method provides. However, this can only be done if we stack data on anywhere from 20 ~ 100 clusters, each needing an array of independent realizations of different N, quickly creating the need for high performance computing to solve the issue. By running ~2,000 in-parallel jobs on the FLUX high performance computer, we successfully show that within the Millennium Simulation, the stacking method applied to the caustic technique can increase the precision of an ensemble cluster mass estimate by upwards of 60%, and increase the accuracy by upwards of 50%. |
| 32 | Numerical simulations of accretion outbursts during low-mass star formation |
Jaehan Bae, Lee Hartmann, Zhaohuan Zhu, Richard Nelson |
Astronomy | During their formation, low-mass (< 1 solar mass) protostars are thought to “episodically” accrete material in large, rapid bursts from their circumstellar disks. While such events have been observed over the last several decades, their mechanism(s) by which these accretion bursts occur is still under debate. In this study, we use 2D Fast Advection in Rotating Gaseous Objects (FARGO) code to model accretion bursts driven by magnetorotational instability (MRI), which successfully reproduce some observed outburst properties. |
| 33 | Thermal Buckling of Composite Plates with Spatially Varying Fiber Orientations |
Adam Duran, Nicholas Fasanella |
Aerospace Engineering |
Thermal buckling analysis of square composite laminates with variable stiffness properties is presented. Fiber angles vary spatially and result in material properties that are a function of position. The critical buckling temperature for such laminates were obtained based on classical lamination theory and the Galerkin finite element method using Kirchhoff plate elements. In our approach, we discretize the domain to transform nonlinear fiber path functions to linear piecewise functions so we may apply classical lamination theory. Using this method, thermal responses for symmetric balanced simply supported laminates under constant thermal load were investigated and the optimal angles and fiber paths to resist thermal buckling were obtained. Validation for the method presented is achieved using the results for the special case of constant fiber angles found in literature. We found that angle configurations exist that provide higher resistance to thermal buckling in comparison to straight fiber configurations. |
| 34 | Target research areas for High Throughput Nuclear Architecture Analysis (HTNAA) |
Ari Allyn-Feuer, David Dilworth, Walter Meixner, Alex Ade, and Brian Athey |
DCMB | Researchers at the University of Michigan, in collaboration with the Brady Institute at Johns Hopkins University, have begun development of a high-throughput, semi-automated cell analysis system to characterize a number of cellular features including chromosome territory boundaries and their three-dimensional arrangement within the cell nucleus. Image data acquired with confocal and super-resolution imaging techniques will be processed with an image processing pipeline facilitating tasks such as channel separation, interest operations and classification, spectral unmixing, clustering, data fusion, feature extraction, and visualization across large populations of cells. This information, collected in an appropriate biocellular system with appropriate probesets, can be used to interrogate biologically and clinically relevant hypotheses by defining and calculating derived data forms tailored to the investigation. Here we explore four major research areas which will benefit from the application of HTNAA techniques: Transcription Factory research, the validation of Hi-C results, Prostate Cancer progressivity prognosis, and the exploration of Glucocorticoid receptor mediated stress diseases. We also explore the derived data forms which will contribute to these investigations. |
| 35 | Improving Quality of Service and Fairness in Stochastic Operating Room Planning |
Yan Deng, Siqian Shen, Brian Denton |
IOE | Existing research on stochastic operating room (OR) planning rely on an expected-cost-based approach, which penalizes under-performance and minimizes the total expected cost. However, penalty costs are often difficult to estimate accurately. In this paper, we formulate the problem as chance-constrained programs, where waiting and overtime are limited by probabilities. Chance constraint provides a natural representation of Quality of Service parameters. It also promotes fairness by accounting for individual surgeries and ORs. We consider a set of surgeries with uncertain duration and a set of ORs with fixed length of opening time, we decide (i) which ORs to open, (ii) surgery-to-OR allocation and (iii) starting time of individual surgeries, to minimize OR opening cost. We formulate chance constraints to capture the on-time-start guarantees for individual surgeries and the on-time-closure target of the entire OR sector. We provide two alternative models where surgeries are scheduled in continuous time and discrete time blocks respectively. Via sampling, we reformulate chance constraints in each model as mixed-integer programming (MIP) constraints based on finite realizations of uncertain surgery duration. We also decompose the discrete-time MIP and develop cutting planes to improve the computational efficiency. We test randomly generated instances based on real data from a healthcare provider and develop insights of how our approaches improve the quality of service and fairness in stochastic OR planning. |
| 36 | Characterization of Macrolesions Induced by Therapeutic High Intensity Myocardial Contrast Echocardiography |
Yiying I. Zhu, Douglas L. Miller, Chunyan Dou and Oliver D. Kripfgans |
Biomedical Engineering |
Hypertrophic cardiomyopathy (HCM) can lead to sudden death in young adults without prior indication. Echocardiography can be used to diagnose HCM. Current treatment is highly invasive and inefficacious. Contrast echocardiography performed using therapeutic intensities is hypothesized to induce sparse microlesions and treat the heart muscle non-invasively. Contrast-enhanced pulsed ultrasound was employed to treat HCM with The treatment volume (macrolesion) was characterized with automatic |
| 37 | Molecular Dynamics Simulations of Compressive Yielding of Cross-Linked Epoxies |
Nicholas Fasanella,Abhishek Kumar, Veera Sundararaghavan |
Aerospace Engineering |
Molecular dynamics simulations are performed to study compressive yielding behavior of DGEBA/DDS cross-linked epoxy in a low temperature glassy state. The molecular model was built using the dendrimer approach and a representative epoxy structure was built by subjecting the structure to several thermal annealing cycles. The simulations show that the stresses drop sharply at the yield point, which was identified to occur due to the activation of wedge disclinations of the epoxy chain. The chemistry and geometry (critical segment length, angles, bond torsions) involved in the molecular mechanism of compressive yielding have been measured. The results are analyzed in the context of Argon theory, which is a linear elastic model of a wedge disclination, and gives a critical kinking segment length that agrees well with the molecular simulations. The yield stress versus temperature predictions of Argon theory were directly compared with molecular simulation results and show good correlation. Finally, the use of Argon theory for extracting yield stresses at quasi-static strain rates from high rate molecular simulations was investigated. |
| 38 | Monte-Carlo Simulation Of Lamellar Grain Structure Arrangement using Discrete Dislocation Plasticity |
Sriram Ganesan, Dai Okumura |
Aerospace | Material strengthening can be achieved by effectively obstructing the dislocation motion. The mean grain size in polycrystalline materials, d, plays a critical role in mechanical properties as the grain boundary limits the free path of moving dislocations. In the present work, Monte-Carlo simulation of polycrystal plasticity is performed using 2-D discrete dislocation approach, to find the effect of grain size on λ, the mean distance of dislocation activation across the grain boundary. The approach is based on applying periodic homogenization to the superposition method of Van der Giessen and Needleman, and on decomposing displacements into macro and perturbed components. The parallelized code is implemented to obtain λ for layer thickness of 1-10 μm for two different grain structure arrangements. The results indicate that λ is very sensitive to grain structure arrangement and is one order lower in lamellar arrangement when compared with other grain structure arrangements and experimental results on pure Aluminum specimens. |
| 39 | Towards Quantitative Dynamical Mean Field Theory |
Yao Li*Dominika Zgid |
Chemistry | In a quantum mechanical description of the Kondo effect, the single spin of the magnetic atom or molecule creates a loosely bound electronic open-shell singlet with one of the electrons from the metallic surface. A sharp resonance, the Kondo resonance, present near the Fermi level is an experimental manifestation of this effect. The Kondo effect is profoundly interesting because the arising magnetic behavior comes from few unpaired electrons and, therefore, is of purely quantum multi-determinantal nature and cannot be simulated successfully by one-body approximations such as Hartree-Fock (HF) or Density Functional Theory (DFT) with approximate functionals. In order to perform quantitative calculations of the Kondo phenomena we use Dynamical Mean Field Theory (DMFT) that can be understood as a QM/QM embedding method and therefore allows us to correctly describe the coupling between the magnetic atom d-electrons and the surface conduction band. We will present newly developed updates to the DMFT method that allow us to calculate the Kondo problem in an ab-initio manner without any experimental parametrization. This approach will be tested on a series of magnetic atoms and molecular systems deposited on a graphene in order to calculate Kondo temperatures and photoelectron spectra. |
| 40 | How to make dynamical mean-field theory applicable to large realistic systems? |
Alexei A. Kananenka, Dominika Zgid |
Chemistry | Transition metal oxide perovskites are very challenging to describe due to the presence of strong correlation among the orbitals. However, not all the orbitals in these difficult systems are equally correlated. We aim to design a computational method that could exploit some simplifying features in the structure of electron correlation. To classify different types of electron correlation we study the Hubbard model. We propose a procedure for selecting the most important orbitals within this model. In the basis of natural orbitals, the orbitals of the two-dimensional Hubbard model are not occupied equally. This observation is the basis of our method and we employ the modified iterative perturbation theory (MPT) — an efficient and relatively simple approach to find orbitals populations. Subsequently, for the subset of the most correlated orbitals we use dynamical mean-filed theory (DMFT) to provide a more accurate answer for the most important orbitals in the problem. This procedure is general and in the future we plan to apply it to realistic solids. |
| 41 | Robust Algorithms for Limited Angle Imaging of Ultrasound Propagation and Attenuation |
Nneka Richards, Rungroj Jintamethasawat, Fong Ming Hooi, Paul Carson |
Applied Physics |
Speed of sound (SOS) and the acoustic attenuation coefficient are the simplest two properties that can be extracted from breast tissue in the mammographic geometry to improve cancer detection. The objective of this project is to develop and employ robust algorithms for computing SOS and attenuation images. Limited angle tomography of SOS imaging can be corrected for proper spatial distribution and quantitative values by utilizing a covariance matrix derived from a standard ultrasound image. Feature processing and image reconstruction is computationally demanding, though the use of highly parallel algorithms reduces the runtime to approximately 1.5 hours. The employed shared memory model uses approximately 1 GB of RAM per node for a total 300 MB of raw input data. Both indicate relatively low demand on memory resources but high demand on CPU. Calculated SOS in ultrasound phantoms is within 1% compared to true SOS. Knowledge of SOS and attenuation will improve characterization of benign and malignant tissues. |
