## Kunihiko (Sam) Taira

Friday, April 23, 2021

**Title:**Network-Based Characterization, Modeling, and Control of Fluid Flows

**Abstract:**The network of interactions in a sea of vortices gives rise to the amazingly rich dynamics of fluid flows. To describe these interactions, we consider the use of mathematical tools from the emerging field of network science that is comprised of graph theory, dynamical systems, data science, and control theory. In this presentation, we discuss ways to describe unsteady fluid flows with vortical-interaction, modal-interaction, and probability-transition networks. The insights gained from these formulations are used to characterize, model, and control laminar and turbulent flows. We will also discuss some of the challenges of applying network based techniques to fluid flows and the prospects of addressing them through data-inspired techniques.

## Jin Liu

Friday, April 16, 2021

**Title:**Modeling and simulations of protein conformational changes and virus entry

**Abstract:**Virus infections remain major threats to human health worldwide. Viruses are intracellular parasites, and must enter host cells and deliver their genetic material to initiate infection. Virus entry is a highly complex process that may involve hundreds of trans-membrane and peripheral membrane proteins. This highly complex process is dictated by various events, such as virus motion, membrane deformation and merging as well as molecular scale protein-protein, protein- lipid interactions and drastic protein conformational changes, occurring at multiple stages and at multiple length and time scales. The question of how these biochemical and biomechanical events work together culminating in productive entry is not well understood but fundamentally important for development of vaccine candidates and identification of new targets for inhibitor design. Modeling and simulations of virus entry at different scales can provide mechanistic insights into this complex process. In this talk, we will present our recent simulation research on membrane deformation and protein conformational changes for virus entry. A mesoscale stochanstic membrane model has been implemented to investigate the membrane deformations during the entry process. We will also discuss our development of the coase-grained force field to capture the protein conforamtional changes, and the on-going work of machaine-learning facilitated sampling of protein structures.

## Sapna Sarupria

Friday, April 2, 2021

**Title**: Molecular Engineering of Ice Responsive Materials: Decoding Heterogeneous Ice Nucleation**Abstract:**The presence of particles such as dust and pollen affect cloud microphysics significantly through their effect on the state of water. These particles can hinder or accelerate the liquid-to-solid transition of water, and also affect the ice polymorph formed in the clouds. This indirectly cloud reflectivity, cloud lifetime, and precipitation rates. While a predominant phenomenon, the understanding of the surface factors that affect ice nucleation is minimal. In our research, we use molecular simulations to illuminate the pathways through which surface properties influence ice nucleation. Experiments cannot probe the length and time scales relevant to nucleation. While molecular simulations, in principle, can probe the length and time scales of nucleation, in practice nucleation is challenging to sample. Nucleation is often associated with large free energy barriers and thus, is difficult to sample in straightforward simulations. Advanced sampling techniques and other creative approaches are needed. In this talk, I will discuss the insights we have obtained on heterogeneous ice nucleation through studies of three surfaces – silver iodide, kaolinite and mica. I will also highlight the synergistic combination of experiments and simulations in understanding heterogeneous ice nucleation. I will introduce a recently developed method in our group facilitate computational studies of heterogeneous nucleation. I will conclude by providing a perspective on the broader implications of our studies on interfacial phenomena and surface design.

## Zachary Ulissi

Friday, March 26, 2021

**Title:**Workflows, Datasets and Models for Active Discovery in Catalysis**Abstract:**Machine learning accelerated catalyst discovery efforts has seen much progress in the last few years. Datasets of computational calculations have improved, models to connect surface structure with electronic structure or adsorption energies have gotten more sophisticated, and active learning exploration strategies are becoming routine in discovery efforts. However, there are several large challenges that remain: to date, models have had trouble generalizing to new materials or reaction intermediates and applying these methods requires significant training. I will review and discuss methods in my lab for high-throughput catalyst screening and on-line discovery of interesting materials, resulting in an optimized Cu-Al catalyst for CO2-to-ethylene conversion. I will then introduce the Open Catalyst Project and the Open Catalyst 2020 dataset, a collaborative project to span surface composition, structure, and chemistry and enable a new generation of deep machine learning models for catalysis, with initial results for state-of-the-art deep graph convolutional models. Finally, I will discuss on-going work to develop small ML models to accelerate routine calculations without requiring expert intervention.**Bio:**Zachary Ulissi is an Assistant Professor of Chemical Engineering at Carnegie Mellon University. He works on the development and application of high-throughput computational methods in catalysis, machine learning models to predict their properties, and active learning methods to guide these systems. Applications include energy materials, CO2 utilization, fuel cell development, and additive manufacturing. He has been recognized nationally for his work including the 3M Non-Tenured Faculty Award and the AIChE 35-under-35 award among others.

## Nicole Yunger Halpern

**February 26, 2021**

**Title:**Learning about learning by many-body systems

**Abstract:**Many-body systems from soap bubbles to suspensions to polymers learn the drives that push them far from equilibrium. This learning has been detected with thermodynamic properties, such as work absorption and strain. We progress beyond these macroscopic properties that were first defined for equilibrium contexts: We quantify statistical mechanical learning with representation learning, a machine-learning model in which information squeezes through a bottleneck. We identify a structural parallel between representation learning and far-from-equilibrium statistical mechanics. Applying this parallel, we measure four facets of many-body systems’ learning: classification ability, memory capacity, discrimination ability, and novelty detection. Numerical simulations of a classical spin glass illustrate our technique. This toolkit exposes self-organization that eludes detection by thermodynamic measures. Our toolkit more reliably and more precisely detects and quantifies learning by matter.

## Elbridge Gerry Puckett

**February 19, 2021**

**Title:**Recent Advances in Modeling Subduction and Viscoelastic Flow in Geodynamic Computations.**Abstract:**We will describe two separate but related methodologies that have been implemented in the open source, finite element code ASPECT, which computational geophysicists use to model a wide variety of problems that arise in Earth and Planetary geophysics. The first technique is a volume-of-fluid (VOF) interface tracking algorithm that was originally designed to model the subduction of the oceanic lithosphere of a tectonic plate beneath a less dense lithosphere of a second plate. However, we have since used this VOF methodology to model some basic laboratory experiments in order to benchmark some of the rheological models that have been implemented in ASPECT. The second technique is the particle or particle-in-cell (PIC) methodology, which we have been developing and benchmarking for use in ASPECT for the past seven years or so. This PIC methodology has been shown to have excellent weak and strong scaling over at least three orders of magnitude of model size on a uniform grid. In addition, our PIC algorithm shows that strong scaling for the adaptive grid case is nearly as good as for the uniform grid case, decreasing the total runtime essentially linearly from 96 to 3,072 cores. We will briefly show a collection of benchmarks we have used and developed to assess the accuracy of this PIC methodology and conclude with a description and video of a beam bending in a less dense viscoelastic medium due to the force of gravity in which the viscoelastic rheology is modeled by the components of stress that are carried on the particles and interpolated onto the underlying finite element grid at each time step.

## Steven L. Brunton

**January 22, 2021**

**Title:**Machine learning for Fluid Mechanics

**Abstract:**Many tasks in fluid mechanics, such as design optimization and control, are challenging because fluids are nonlinear and exhibit a large range of scales in both space and time. This range of scales necessitates exceedingly high-dimensional measurements and computational discretization to resolve all relevant features, resulting in vast data sets and time-intensive computations. Indeed, fluid dynamics is one of the original big data fields, and many high-performance computing architectures, experimental measurement techniques, and advanced data processing and visualization algorithms were driven by decades of research in fluid mechanics. Machine learning constitutes a growing set of powerful techniques to extract patterns and build models from this data, complementing the existing theoretical, numerical, and experimental efforts in fluid mechanics. In this talk, we will explore current goals and opportunities for machine learning in fluid mechanics, and we will highlight a number of recent technical advances. Because fluid dynamics is central to transportation, health, and defense systems, we will emphasize the importance of machine learning solutions that are interpretable, explainable, generalizable, and that respect known physics.

## Arvind Gopinath

**December 4, 2020**

**Title:**Swarming bacteria as novel active biomaterials – insights into the collective mechanics, particle transport and morphological adaptation in swarming bacteria from*in-silico*experiments.**Abstract:**Flagellated and motile bacteria, in isolation or in coexistence with fungi, are implicated in about two-thirds of human infections. During infection, and generally even in relatively benign situations, bacteria may colonize surfaces via a process called swarming – a form of rapid translocation associated with changes in cell phenotype. As swarmer cells move rapidly, they interact with each other forming cohesive structures that then rapidly develop into collective multicellular aggregates. Understanding the swarming process is important for biomedicine, and is relevant to evolutional biology – in, for instance, understanding the evolution of phylogenetic spatial structures in bacterial populations. On a complementary note, understanding the biophysical and mechanical aspects of swarming can provide insights into synthesizing the next generation of adaptable matter. While comprised of independently cells, swarms exhibit collective properties and remarkable emergent flow patterns. Recent work supports treating these collective systems as novel living biomaterials with evolving composite properties. In this talk, I will discuss how the combination of key experimental discoveries combined with multi-scale simulations enables careful interrogation, analysis and understanding of microbial swarms and films. The experimental component of the talk will highlight experimental observations on swarming*Serratia marcescens*, a rod-shaped gram negative bacterium. Following that, I will discuss recent work on a suite of computational approaches that we exploit to simulate these active systems. Our approaches include agent-based full-hydrodynamics simulations, adaptations of Active Brownian Particle (ABP) stochastic models, and mean-field continuum models solved using parallellized level-set methods on high resolution and highly adaptive Quadtree meshes.

## Boris Kozinsky

**November 20**, **2020**

**Title:**Designing energy conversion materials with ab-initio and active machine learning computations of electron-phonon and ion dynamics.**Abstract:**Accurate atomistic computations of transport and reaction dynamics are an important challenge and an opportunity for designing materials for energy conversion and storage. In the context of thermoelectric materials, we develop new automatable computational methods for describing electron-phonon scattering dynamics. By predicting electrical transport properties, we computationally discovered several new low-cost thermoelectric alloys with record device performance. In the context of solid-state batteries, computations of ionic transport reveal how strong ionic interactions lead to disorder and surprising collective phenomena in amorphous polymer electrolyte materials and enable us to design new electrolyte chemistries. High-fidelity ab-initio simulations of atomistic dynamics are limited to small systems and short times, and development of surrogate machine learning models for force fields is an emerging promising direction to access long-time large-scale dynamics of complex materials systems. However, the main challenges are high accuracy, reliability, and computational efficiency of these models, which critically depend on the training data sets. We develop ML interatomic potential models that are interpretable and uncertainty-aware, and orders of magnitude faster than reference quantum methods. Principled uncertainty quantification built into these models enables the construction of autonomous data acquisition schemes using active learning. We demonstrate on-the-fly learning of machine learning force fields and use them to gain insights into previously inaccessible physical and chemical phenomena in ion conductors, catalytic surface reactions, 2D materials phase transformations, and shape memory alloys.

## Ken Kamrin

**November 6, 2020**

**Title:**Simulating solids like fluids: A fully Eulerian approach to fluid-structure interaction**Abstract:**Fluids and solids tend to be addressed using distinct computational perspectives. Solid deformation is most commonly simulated with Lagrangian finite-element methods, whereas fluid flow is amenable to Eulerian-frame approaches such as finite difference and finite volume methods. Problems that mix fluid and solid behaviors simultaneously present interesting numerical challenges. Here we focus on fluid-structure interaction (FSI) problems, and discuss an emerging method called the Reference Map Technique, which allows us to simulate deformable solids on a fixed Eulerian grid. The key is to store and update the reference map field on the grid, which tracks the inverse motion. Using this technique to represent the solid phase, we can solve all phases of an FSI problem on a single fixed grid using fast update procedures very similar to those used in two-phase Navier-Stokes fluid simulations. Various solid constitutive behaviors can be used, such as nonlinear elasticity and plasticity. Systems of many submerged and interacting solids can be simulated, and, by activating the solids internally, we can simulate systems of “soft swimmers”. Incompressibility and rigidity constraints can be applied in all phases by adopting Eulerian projection approaches commonly used in CFD. The addition of the reference map field to the grid also presents certain benefits when computing level-set interface advection, including a procedure to guarantee mass conservation.

## Ivan Bermejo-Moreno

**October 30, 2020**

**Title:**Shock-induced turbulent mixing and interactions with flexible panels through simulations**Abstract:**Two fundamental challenges that arise in the development of air-breathing supersonic combustion ramjet engines (scramjets) for hypersonic flight are: 1) the rapid mixing of fuel and oxidizer that must occur prior to combustion, and 2) the coupling between the engine structure and the flow dynamics. Interactions of shock waves and turbulence that characterize the flow inside scramjets play a key role in both mixing enhancement and aerostructural coupling. We present ongoing efforts on the high-fidelity numerical simulation of these two phenomena. Scalar mixing under canonical shock-turbulence interactions will be addressed first by means of Direct Numerical Simulation, evaluating the effects of variations in the relevant physical parameters: shock and turbulence Mach numbers, Reynolds number, and Schmidt numbers. The analysis will highlight changes along the shock-normal direction of scalar variance and dissipation-rate budgets, flow topology, and alignments of the scalar gradient with vorticity and strain-rate eigendirections. Then, we will focus on interactions of shock waves reflecting off turbulent boundary layers that develop along the walls of the scramjet. Rigid and flexible walls will be considered, by coupling a wall-modeled large-eddy simulation solver for the fluid flow with an elastic solid structural solver that accounts for geometric nonlinearities. We will emphasize strong shock/boundary-layer interactions resulting in mean flow separation and low-frequency unsteadiness that can interact with natural frequencies of the structure.

## Michael Graham

**October 23, 202**0

**Title:**Data-driven model reduction and multiscale modal decomposition for complex chaotic systems**Abstract:**Many complex nonequilibrium systems, including turbulent flows, are characterized by chaotic dynamics, a large number of degrees of freedom, and hierarchical, multiscale structure in space and time. In two vignettes, we describe some recent work aimed at developing and applying machine learning and data science tools for systems displaying these characteristics. The first vignette builds on the idea that while partial differential equations are formally infinite- dimensional, the presence of energy dissipation drives the long-time dynamics onto a finite-dimensional invariant manifold sometimes called an inertial manifold (IM). We describe a data-driven framework to represent chaotic dynamics on this manifold and illustrate it with data from simulations of the Kuramoto-Sivashinsky equation. A hybrid method combining linear and nonlinear (neural-network) dimension reduction transforms between coordinates in the full state space and on the IM. Additional neural networks predict time evolution on the IM; this can be done in either the discrete-time (difference equation) or continuous-time (ordinary differential equation) setting. The formalism accounts for translation invariance and energy conservation, and substantially outperforms linear dimension reduction, reproducing very well key dynamic and statistical features of the attractor. The second vignette addresses how to represent flow or other fields with multiscale structure. We describe a method, inspired by wavelet analysis, that adaptively decomposes a dataset into an hierarchy of structures (specifically orthogonal basis vectors) localized in scale and space: a “data-driven wavelet decomposition”. This decomposition reflects the inherent structure of the dataset it acts on. In particular, when applied to turbulent flow data, it reveals spatially localized, self-similar, hierarchical structures. It is important emphasize that self-similarity is not built into the analysis, rather, it emerges from the data. This approach is a starting point for the characterization of localized hierarchical turbulent structures that we may think of as the building blocks of turbulence. It will also find application to other systems, such as atmospheres, oceans, biological tissues, active matter and many others, that display multiscale spatiotemporal structure.

## Christopher Jarzynski

**October 9, 2020**

**Title:**Scaling down the laws of thermodynamics**Abstract:**Thermodynamics provides a robust conceptual framework and set of laws that govern the exchange of energy and matter. Although these laws were originally articulated for macroscopic objects, nanoscale systems also exhibit “thermodynamic-like” behavior – for instance, biomolecular motors convert chemical fuel into mechanical work, and single molecules exhibit hysteresis when manipulated using optical tweezers. To what extent can the laws of thermodynamics be scaled down to apply to individual microscopic systems, and what new features emerge at the nanoscale? I will describe some of the challenges and recent progress – both theoretical and experimental – associated with addressing these questions. Along the way, my talk will touch on non-equilibrium fluctuations, “violations” of the second law, the thermodynamic arrow of time, nanoscale feedback control, strong system-environment coupling, and quantum thermodynamics.

## Rafael Gomez-Bombarelli

**October 2nd, 2020**

**Title:**Fusing machine learning and atomistic simulations for materials design**Abstract:**Data-driven approaches match or outperform humans at a number of tasks, including pattern recognition in images and text or planning and strategy in rule-based games. The application of machine learning techniques is also promising for accelerating materials design. However, experimental data for training is typically scarce and sparse. The interplay between physics-based simulations and data-driven models is particularly advantageous. It allows relying on transferable laws rather than only fitting data in a black box fashion. Meanwhile, learning from data provides a unique opportunity to parameterize and augment physics-based models, or completely replace them. Models can be built that map the structure and composition of materials to their properties. With such models, it is then possible to rapidly screen libraries of candidate materials for a desired application before going to the lab. Generative models go one step further and allow tackling the inverse problem: given the desired property, automatically suggesting a new optimal material that achieves it. How to represent matter so that it can be read into or written by a computer program is key for these coupled tasks of property prediction and materials optimization. Strategies are needed to represent materials in a machine-readable way that is data-efficient, expressive, respectful of physical invariants and, ideally, invertible. Here, we will discuss our current efforts in building bottom-up atom-level representations for materials design. These include variational autoencoders for dimensionality reduction and inverse design in molecules and polymers, representation and unsupervised learning for graphs and sequences in crystals and polymers, generative models to accelerate Monte Carlo simulations of alloy phase diagrams or end-to-end differentiable simulations.

### Cesar de la Fuente

** February 28th, 2020 **

**Title**: Towards Computer-Made Antibiotics-
**Abstract:**Until now, the natural world has supplied us with antibiotics. Bacteria, however, are increasingly resistant to these drugs. The next generation of antibiotics will likely come not from nature but from computer-based discovery. Working at the forefront of this development, I seek to harness computational power to find molecules with antibacterial activity. I use synthetic biology and computational tools to determine features contributing to this activity and train computers to find— or design— candidate molecules and tweak their structures virtually. Experimentation is reserved for validating computer predictions, saving time, labor, and expense. With machine-based molecular discovery, I explore proteins and peptides as engineering scaffolds. My approaches diversify proteins, such as host defense peptides (HDPs), beyond their natural variation. For example, to increase their antimicrobial properties, we trained a computer to execute a fitness function that selects for structures that interact with bacterial membranes, thereby converting several HDPs into the first artificial antimicrobials that kill bacteria both in vitro and in animals. By investigating these exciting possibilities, I aim to build machine-made antibiotics to combat infectious diseases and develop clinical applications for autonomously generated synthetic molecules. Computer-made drugs may help to replenish our arsenal of effective drugs and outpace the evolution of antibiotic resistance.

### Sam Schoenholz – Google Brain

February 21, 2020

**Title:**JAX MD: Accelerated, Differentiable, Molecular Dynamics-
**Abstract:**A large fraction of computational science involves simulating the dynamics of particles that interact via pairwise or many-body interactions. These simulations, called Molecular Dynamics (MD), span a vast range of subjects from physics and materials science to biochemistry and drug discovery. Most MD software involves significant use of handwritten derivatives and code reuse across C++, FORTRAN, and CUDA. In this talk I will describe substantial recent advances in software that has taken place in machine learning. I will then go on to discuss how we can leverage these advances to improve simulations in Physics with a focus on MD. To that end I will introduce JAX MD, an end-to-end differentiable MD package written entirely in Python that can be just-in-time compiled to CPU, GPU, or TPU. JAX MD allows researchers to iterate extremely quickly and to easily incorporate machine learning models into their workflows. Finally, since all of the simulation code is written in Python, researchers can have unprecedented flexibility in setting up experiments without having to edit any low-level C++ or CUDA code. In addition to making existing workloads easier, JAX MD allows researchers to take derivatives through whole-simulations as well as seamlessly incorporate neural networks into simulations.

### Yoichiro Mori – University of Pennsylvania & the University of Minnesota

January 31, 2020

**Title:**Mathematical Justification of Slender Body Theory**Abstract:**Systems in which thin filaments interact with the surrounding fluid abound in science and engineering. The computational and analytical difficulties associated with treating thin filaments as 3D objects has led to the development of slender body theory, in which filaments are approximated as 1D curves in a 3D fluid. In the 70-80s, Keller, Rubinow, Johnson and others derived an expression for the Stokesian flow field around a thin filament given a one-dimensional force density along the center-line curve. Through the work of Shelley, Tornberg and others, this slender body approximation has become firmly established as an important computational tool for the study of filament dynamics in Stokes flow. An issue with slender body approximation has been that it is unclear what it is an approximation to. As is well-known, it is not possible to specify some value along a 1D curve to solve the 3D exterior Stokes problem. What is the PDE problem that slender body approximation is approximating? Here, we answer this question by formulating a physically natural PDE problem with non-conventional boundary conditions on the filament surface, which incorporates the idea that the filament must maintain its integrity (velocity along filament cross sections must be constant). We prove that this PDE problem is well-posed, and show furthermore that the slender body approximation does indeed provide an approximation to this PDE problem by proving error estimates. This is joint work with Laurel Ohm, Will Mitchell and Dan Spirn.

### Professor Mark E. Tuckerman of New York University

November 22, 2019

**Title:**“Molecular Simulation and Machine Learning as Routes to Exploring Structure and Phase Behavior in Atomic and Molecular Crystals”-
**Abstract:**Organic molecular crystals frequently exist in multiple forms known as polymorphs. Structural differences between crystal polymorphs can affect desired properties, such as bioavailability of active pharmaceutical formulations, lethality of pesticides, or electrical conductivity of organic semiconductors. Crystallization conditions can influence polymorph selection, making an experimentally driven hunt for polymorphs difficult. Such efforts are further complicated when polymorphs initially obtained under a particular experimental protocol “disappear” in favor of another polymorph in subsequent repetitions of the experiment. Consequently, theory and computational can potentially play a vital role in mapping the landscape of crystal polymorphism. Traditional crystal structure prediction methods face their own challenges, and therefore, new approaches are needed. In this talk, I will show, by leveraging concepts from mathematics and statistical mechanics in combination with techniques of molecular simulation, traditional methods, and machine learning, that a new paradigm in crystal structure prediction may be emerging. Examples demonstrating prediction of structures of crystals, co-crystals, and phase transitions will be presented.

### Associate Professor Karin Leiderman of the Colorado School of Mines

November 8, 2019

**Title:**“Title: “Mathematical Modeling of Thrombin-Fibrin Binding Dynamics”**Abstract:**Blood clot formation involves the coupled processes of platelet aggregation and coagulation, which are triggered when there is break in a blood vessel. Platelet aggregation is largely a physical process while coagulation is biochemical, consisting of a large network of reactions that culminate in the generation of the enzyme thrombin. Thrombin cleaves fibrinogen into fibrin, which polymerizes into fibers to form a stabilizing gel matrix in and around growing platelet aggregates. Thrombin also (re)binds directly to fibrin but this interaction, and its purpose, is not fully understood. Thrombin-fibrin binding is often described as two independent, single-step binding events, one high-affinity and one low-affinity, each through a different exosite on thrombin. However, kinetic schemes describing these single-step binding events with reported kinetic rate constants cannot explain experimentally-observed residency times of fibrin-bound thrombin. In this work, we study a bivalent, sequential-step binding scheme as an alternative to the high-affinity event, and in addition to the low-affinity one. We developed mathematical models for the single- and sequential-step schemes consisting of reaction-diffusion equations to compare to each other and to previously published experimental data. We then used Bayesian inference, in the form of Markov Chain Monte Carlo, to learn model parameter distributions from the experimental data. For the model to best fit the data, we needed an additional assumption that thrombin was irreversibly sequestered; we hypothesized that this could be due to thrombin becoming physically trapped within fibrin fibers as they formed. We also discuss how our model can be used to further probe scenarios dealing with thrombin allostery.

### Professor Chunlei Liang of Clarkson University

November 1, 2019

**Title**: “High-order Spectral Difference Method for Studying Marine Hydrodynamics and Thermal Convection and Magneto-hydrodynamics for the Sun”-
**Abstract:**Two recent advancements of high-order spectral difference (SD) method for computational fluid dynamics on unstructured meshes will be presented. The first progress is our contribution to a new curved sliding-mesh approach to the SD method for simulating flapping and rotary wing aerodynamics. The second elevation of the SD method is our recent successful design of a massively parallel code, namely CHORUS, for predicting thermal convection in the Sun. Recently, we have also built a simulation capability for predicting magnetohydrodynamics of the Sun.

### Professor Yuri Bazilevs of Brown University

October 25, 2019

**Title:**Isogeometric Methods for Solids, Structures, and Fluid-Structure Interaction: From Early Results to Recent Developments**Abstract:**This presentation is focused on Isogeometric Analysis (IGA) with applications to solids and structures, starting with early developments and results, and transitioning to more recent work. Novel IGA-based thin-shell formulations are discussed, and applications to progressive damage modeling in composite laminates due to low-velocity impact and their residual-strength prediction are shown. Fluid–structure interaction (FSI) employing IGA is also discussed, and a novel framework for air-blast-structure interaction (ABSI) based on an immersed approach coupling IGA and RKPM-based Meshfree methods is presented and verified on a set of challenging examples. The presentation is infused with examples that highlight effective uses of IGA in advanced engineering applications.

### Professor Youping Chen of the University of Florida

October 4, 2019

**Title:**Concurrent Atomistic-Continuum Modeling and Simulation of Transport Processes in Crystalline Materials**Abstract:**In this talk we present a concurrent atomistic-continuum (CAC) method for modeling and simulation of transport processes in crystalline materials. The CAC formulation extends the Irving-Kirkwood procedure for deriving transport equations and fluxes for homogenized molecular systems to that for polyatomic crystalline materials by employing a concurrent two- level structural description of crystals. A multiscale representation of conservation laws is formulated that holds instantaneously, as a direct consequence of Newton’s second law, using the mathematical theory of generalized functions. Finite element (FE) solutions to the conservation equations, as well as fluxes and temperature in the FE representation, are introduced, followed by numerical examples of atomic-scale structures of interfaces, dynamics of fracture and dislocations, and phonon transport in multiscale structured materials. In addition to providing a methodology for concurrent multiscale simulation of transport processes under a single theoretical framework, the CAC formulation can also be used to compute fluxes (stress and heat flux) in atomistic and coarse-grained atomistic simulations.

### Professor Ahmed Ettaf Elbanna of the University of Illinois

April 19, 2019

**Title:**Topology, Geometry, and Fracture in Networked Materials: A tale of Scales**Abstract:**The skeleton of many natural and artificial structures may be abstracted as networks of nonlinearly interacting elements. Examples include rubber, gels, soft tissues, and lattice materials. Understanding the multiscale nature of deformation and failure of networked structures hold key for uncovering origins of fragility in many complex systems including biological tissues and enables designing novel materials. I will start by an overview of our prior work on modeling polymer chains with sacrificial bonds and hidden length; a topological feature that was previously hypothesized to be responsible for increased toughness and fracture resistance in animal bone. Our model combines nonlinear entropic elasticity with transition state theory for bond breakage and formation to predict rate dependence and time dependent healing in these systems in the quasi-1D limit. I will then introduce an extension of this model to a discrete 2D setting (at the scale of 10s of microns) that enables exploring interplay of topological and geometrical features such as coordination number, cross linking density, and disorder with mechanical deformation and fracture. Specifically, we identify a non-monotonic rate dependence of the reaction force and dissipated energy as well as a transition in mode of failure from diffusive to localized with increased pulling rate. Furthermore, we show that networks with small-world architectures, balancing clustering and average pat

### Professor Jindal Shah of Oklahoma State University

April 26, 2019

**Title:****Abstract:**Ionic liquids are substances that are composed entirely of ions. Negligible vapor pressures and the availability of a large number of cations and anions to tune physicochemical and biological properties for a given chemical process have been the primary drivers for research in this field over the last two decades. Majority of these investigations have focused primarily on elucidating changes in the properties of pure ionic liquids by altering the cation, anion or substituents on the ions. Another approach to expand the range of available ionic liquids is to form ionic liquid-ionic liquid mixtures. From a thermodynamic point of view, the knowledge of the extent of non-ideality in these binary ionic liquid mixtures and the molecular level details enable a priori prediction of thermophysical properties of ionic liquid mixture. In this presentation, we will demonstrate that the difference in the molar volume of the ionic liquids forming the mixture and the difference in the hydrogen bonding ability of the anions can serve as metrics for the prediction of non-ideality in the binary ionic liquid systems. Such non-idealities are quantified in terms of the local structural organization of anions around the cation. We will further highlight that these non-native structures lead to a different dissolution mechanism for CO2 in mixtures in comparison to that for pure ionic liquids although the CO2 solubilities obey apparent ideal mixing rule. On the other hand, an examination of NH3 solubility in binary ionic liquid mixtures reveals a non-ideal NH3-solubility behavior.

### Professor Rajat Mittal of Johns Hopkins University

March 15, 2019

**Title:**Coupled Multiphysics Models of Cardiac Hemodynamics: From Fundamental Insights to Clinical Translation**Abstract:**The mammalian heart has been sculpted by millions of years of evolution into a flow pump par excellence. During the typical lifetime of a human, the heart will beat over three billion times and pump enough blood to fill over 60 Olympic-sized swimming pools. Each of these billions of cardiac cycles is itself a manifestation of a complex and elegant interplay between several distinct physical domains including electrophysiology and mechanics of the cardiac muscles, hemodynamics, and flow-induced movement of the cardiac valves. Another multiphysics interaction that is key to hemostasis involves hemodynamics and blood biochemistry. The clotting cascade, which is a natural response to injury, is initiated by a sequence of biochemical reactions that are strongly modulated by the local flow conditions. In this regard, how the chambers and valves of a healthy heart manage to avoid thrombosis, remains an open question. The presence of heart conditions such as myocadial infarction (MI), cardiomyopathies, valve anomalies and atrial fibrillations, disturb the hemostatic balance and can lead to thrombosis with devastating sequalae such as stroke and MI. Computational models for thrombogenesis in the cardiac system have the potential to provide useful insights into this important phenomenon. In the current talk, I will describe high-fidelity chemo-fluidic modeling of thrombogenesis in the left heart and demonstrate how fundamental insights from these studies are translated into clinically relevant metrics. Application of these models to thrombogenesis in transcatheter aortic valves will also be described.

### Professor Gianluca Iaccarino of Stanford University Professor of Mechanical

March 1, 2019

**Title:**Ensemble Computations – Present and Future of Engineering Computing**Abstract:**Computer simulations are pervasive in science and engineering. Computations, together with theoretical analysis and experiments, constitute the foundation for building knowledge, whether it be to investigate a new physical phenomena or to assess the performance of an innovative device. However, a single computation, despite its sophistication and complexity, can rarely provide sufficient and credible evidence to support a decision. To build confidence in computed outcomes, one typically conducts sensitivity analyses, investigates uncertainty, and explores design variations. All these approaches require an ensemble of computations whose results we combine rigorously via statistical analysis or optimization. In this talk we discuss the use of ensemble of simulations for multidisciplinary design under uncertainty; we have developed novel algorithms to handle ensemble with different fidelity implemented in a new programming environment that enables the efficient use of next generation HPC supercomputers. I will also provide a perspective on the present and the future of computational engineering research at Stanford.

### Professor Dennice Gayme of Johns Hopkins University

February 15, 2019

**Title:**Wind farm modeling and control for power grid support**Abstract:**Raditional wind farm modeling and control strategies focus on layout design and maximizing wind power output. However, transitioning into the role of a major power system supplier necessitates new models and control designs that enable wind farms to provide the grid services that are often required of conventional generators. This talk introduces a model-based wind farm control approach for tracking a time-varying power signal, such as a power grid frequency regulation command. The underlying time-varying wake model extends commonly used static models to account for wake advection and lateral wake interactions. We perform numerical studies of the controlled wind farm using a large eddy simulation (LES) with actuator disks as a wind farm model. Our results show that embedding this type of dynamic wake model within a model-based receding horizon control framework leads to a controlled wind farm that qualifies to participate in markets for correcting short-term imbalances in active power generation and load on the power grid (frequency regulation). Accounting for the aerodynamic interactions between turbines within the proposed control strategy yields large increases in efficiency over prevailing approaches by achieving commensurate up-regulation with smaller derates (reductions in wind farm power set points). This potential for derate reduction has important economic implications because smaller derates directly correspond to reductions in the loss of bulk power revenue associated with participating in regulation markets

### Professor Peter DiMaggio, Imperial College London

January 25, 2019

**Title: “**Engineering Next Generation Mass Spectrometry Technologies for Elucidating the Functions of Protein Modifications.”**Abstract:**Over the past two decades, the rapid development of high-throughput technologies within the fields of proteomics and genomics for measuring thousands of biomolecules in a single experiment has shown great promise in transforming our understanding of fundamental biology. Unfortunately, the observation of a two-fold change or greater in a protein is often not a descriptive biomarker for differentiating between normal and diseased states. We now realize that in addition to the number of protein molecules present, we also need to be able measure the various covalent modifications on proteins and understand how this affects their interactions with other proteins, nucleic acids and small molecules within the complex cellular environment. While mass spectrometry (MS) has found widespread success in its ability to quantify changes across thousands of proteins, it does not scale well in measuring protein modifications other than phosphorylation.

### Professor Qiqi Wang of MIT

November 9, 2018

*Title:**“Finding sensitivity in the long-exposure of chaotic dynamical systems”***Abstract:**There are many scientific and engineering applications for the study of how a dynamical system respond to perturbations. When the dynamical system is a computational simulation, these perturbations can be design changes, environmental noise, numerical error, and modeling uncertainties. In this talk, we investigate how a chaotic dynamical system, whose snapshots are extremely sensitive to small perturbations, can have respond smoothly to perturbations in its time-exposure. By time-exposure, we mean long-time-averaged quantities or ensemble-averaged quantities, also known as statistics. We show that many classic concepts and methods for sensitivity and stability analysis do not apply in this study. We then introduce concepts and techniques applicable to chaotic flows, including Lyapunov spectrum analysis and least squares shadowing method. We demonstrate applications of these concepts and technology and illustrate remaining open questions.

### Professor** Frank Brown **of UC Santa Barbara

November 2, 2018

*Title:**“Mechanics and dynamics of simulated membranes: extracting bending moduli and difficulty in the extraction of diffusion coefficients”***Abstract:**Detailed molecular simulations are increasingly used in membrane biophysics to assist in the interpretation of experiments. However, many of the most fundamental physical properties prove difficult to accurately measure in silico due to small system sizes. The membrane bending modulus and lateral diffusion coefficient for proteins/lipids are two such properties. Two different stories will be presented related to the inference (or attempted inference) of these properties from simulation.

### Professor Salvatore Torquato of Princeton University

October 26, 2018

**Title:**“*Large-Scale Density Fluctuations and Hyperuniformity in the Physical, Mathematical and Biological Science”***Abstract:**While there are four commonly observed states of matter (solid crystal, liquid, gas, and plasma), we have known for some time now that there exist many other forms of matter. The hyperuniformity concept provides a unified means to classify crystals, quasicrystals and special disordered systems. Disordered hyperuniform many-particle systems are exotic states of amorphous matter in that they behave more like crystals or quasicrystals in the manner in which they suppress large-scale density fluctuations, and yet are also like liquids and glasses because they are statistically isotropic structures with no Bragg peaks. Thus, disordered hyperuniform systems can be regarded to possess a “hidden order” that is not apparent on short length scales, which apparently endows such material with novel physical properties. I will review the theoretical foundations of hyperuniform states of matter, and then describe a variety of different disordered examples that arise in physics, mathematics and biology.

### PICS Colloquium: Professor David Williams of Penn State

October 19, 2018

**Title:***“The Stabilization of Finite Element Methods for Compressible Flows”***Abstract:**There are many unique challenges in designing robust and high-fidelity methods for solving the compressible Navier-Stokes equations. These challenges can be (at least partially) remedied by embracing methods that are constructed within a rigorous mathematical framework. This is one of the reasons why finite element methods with their strong mathematical foundation are attractive. However, there remain several open questions regarding their suitability for solving the compressible Navier-Stokes equations. In particular, there are questions about the possibility of constructing robust, and versatile stabilization for finite element methods over a wide range of Reynolds and Mach numbers. In this talk, the speaker (David M. Williams) will attempt to discuss the non-linear stability of finite element methods, and in particular `entropy stability’, `L2 stability’, and `kinetic energy stability’. In addition, he will discuss the practical benefits of each type of stability, and recent advances in the development of more stable methods.

### PICS Colloquium: Tianshu Li (The George Washington University)

October 12, 2018

**Title:**“Complex Roles of Surface in Heterogeneous Ice Nucleation: Chemistry, Geometry and Confinement”**Abstract:**Probing crystal nucleation at the molecular level poses a major experimental challenge, due to the ultrafine scale and stochastic nature of nucleation event. Molecular modeling can be a precious tool to help gain valuable insight into this complex process and to test different hypotheses. In this talk I will show how a water/surface model helps unveil the surprisingly rich and complex behaviors of ice nucleation. In particular, heterogeneous ice nucleation is found to exhibit a complex dependence on surface chemistry, surface topography, and confinement, thus explaining why empirical criteria often fail to predict the nucleation efficiency of an ice nucleator. The complex behaviors, however, are also found able to be rationalized through the role of local ordering of water at interface. This gained insight can also help understand the nucleation behaviors in many other materials.

### PICS Colloquium: Andrej Kosmrlj (Princeton University)

September 14, 2018

**Title:**“Phase separation in multicomponent systems”**Abstract:**Multicomponent systems are ubiquitous in nature and display complex phase behavior. For example, recent evidence shows that cellular compartmentalization of several cellular bodies occurs by the formation of membrane-less liquid-like droplets via classical phase separation processes. Furthermore, these droplets can self-organize into hierarchical structures with functional implications for cells and their formation can even be manipulated via optical means. Motivated by these observations we investigate phase separation in systems with N components by using the Flory-Huggins theory of regular solutions. Dr. Kosmrlj will discuss how phase diagrams can be obtained via the convex hull construction of free energy landscapes. In order to investigate how different coexisting domains pack in space and how do they grow and coarsen over time, he used the Cahn-Hilliard formalism. He observed that phase separation sometimes occurs in multiple stages. Dr. Kosmrlj will discuss how we can estimate interfacial tensions and volume fractions of the coexisting phases, which determine the equilibrium morphology of system. Finally, he will comment on the coarsening of coexisting domains, which can exhibit cross-overs between different scaling regimes.

### PICS Colloquium: Alexander Morozov (University of Edinburgh)

April 13, 2018

**Title:**“Collective behaviour of microswimmer suspensions”Recent years witnessed a significant interest in physical, biological and engineering properties of self-propelled particles, such as bacteria or synthetic microswimmers. The main distinction of this ‘active matter’ from its passive counterpart is the ability to extract energy from the environment (consume food) and convert it into directed motion. One of the most striking consequences of this distinction is the appearance of collective motion in self-propelled particles suspended in a fluid observed in recent experiments and simulations: at low densities particles move around in an uncorrelated fashion, while at higher densities they organise into jets and vortices comprising many individual swimmers. Although this problem recieved significant attention in recent years, the precise origin of the transition is poorly understood. In this talk Dr. Morozov will present a numerical method based on a Lattice-Boltzmann algorithm to simulate hydrodynamic interactions between a large number of model swimmers (order 10^5), represented by extended force dipoles. Using this method he will simulate the transition to large-scale structures in dilute suspensions of self-propelled particles and show that, even well below the transition, swimmers move in a correlated fashion that cannot be described by a mean-field approach. He has developed a novel kinetic theory that captures these correlations and is non-perturbative in the swimmer density. To provide an experimentally accessible measure of correlations, Dr. Morozov will calculate the diffusivity of passive tracers and reveal its non-trivial density dependence. The theory is in quantitative agreement with the Lattice-Boltzmann simulations and captures the asymmetry between pusher and puller swimmers below the transition to turbulence. Finally, he will discuss his recent attempts to understand the nature of the ‘turbulent’ state.**Abstract:**

### PICS Colloquium: Gaurav Arya (Duke)

April 6, 2018

**Title:**Molecular-Scale Modeling of Polymer-Nanoparticle Composites**Abstract:**The incorporation of nanoparticles (NPs) into polymers constitutes a powerful strategy for enhancing their thermomechanical properties and for introducing new optical, electrical, and magnetic functionalities into the polymers. This talk reviews our ongoing efforts in modeling the mesoscale morphology, assembly mechanisms, and thermomechanical properties of polymer nanocomposites. I will begin by discussing Monte Carlo simulations of polymer-grafted shaped NPs to elucidate the role played by polymer grafts in dictating the free-energy landscape, assembly pathway, and relative orientation of the NPs in their higher-order aggregates. These results have led to the development of a novel self-assembly strategy for fabricating tunable plasmonic nanojunctions within a polymer thin film. Next, I will describe an approach involving high-throughput quantitative image analysis and lattice models for inferring dynamic parameters of NP assembly from spatially and temporally disjointed images of composites. Application to shaped, metallic NPs reveals a cluster-cluster aggregation mechanism of assembly, where the NPs and their clusters diffuse in a Stokes-Einstein manner and stick with a probability that depends on their size and geometry as well as molecular weight of the surrounding polymer. Finally, I will discuss the application of atomistic and coarse-grained molecular dynamics simulations in predicting thermomechanical properties of polymer nanocomposites, especially viscoelastic behavior in the context of shock-mitigation composites and mechanical flexibility and stability in the context of flexible organic semiconductors.

### PICS Colloquium: Amartya Banerjee

- March 16, 2018

### PICS Colloquium: Catalin Picu (RPI)

- February 16, 2018

### PICS Colloquium: Jutta Rogal (NYU)

February 2, 2018

Atomistic insight into the dynamics and mechanisms of phase transformations in metals**Title:**Understanding the dynamical behaviour of materials is one of the key ingredients to determine materials properties during processing and under service conditions. Obtaining atomistic insight into the fundamental processes and their dynamical evolution up to experimental time-scales remains a great challenge in materials modelling. A particular area of interest are phase transformations that play an essential role in a wide range of materials properties.In this presentation, I will give an overview of our recent progress in the application of advanced atomistic simulations techniques for extended time-scale simulations in materials science. One example are the atomistic rearrangements during solid-solid phase transformations in bulk systems which involve massive structural changes including concerted multi-atom processes. The interface between two structurally different phases leads to a complex energy landscape that needs to be explored during the dynamical evolution of the phase boundary. Here, we employ an adaptive kinetic Monte Carlo (AKMC) approach to investigate such processes at the interface between a body-centred cubic and an A15 phase in molybdenum. A second example is the initial nucleation and growth during solidification in metals. Here, we investigate the atomistic mechanisms of nucleation in nickel for various undercoolings using transition path sampling (TPS). The analysis of the path ensemble reveals a non-classical behaviour with mainly non-spherical nuclei, and shows that the nucleation initiates in regions with high orientational order that also predetermine the selection of specific polymorphs. Including information about the pre-ordering in the undercooled liquid in the reaction coordinate significantly improves the description of the nucleation process, which emphasizes the importance of the pre-ordering in the analysis of the atomistic nucleation mechanism. If the mechanism of the phase transformation is governed by so-called rare events then the timescale of interest will reach far beyond the capabilities of regular molecular dynamics simulations. In addition to the timescale problem the simulations provide a vast amount of data in a high-dimensional space that requires the projection into a low-dimensional space and the identification of suitable reaction coordinates.**Abstract:**

### PICS Colloquium: Padmini Rangamani (UCSD)

November 10, 2017

### PICS Colloquium: Tong Gao (MSU)

November 3, 2017

**Title:**Biomimetic Studies of Fluid-Structure Interaction: Self-Assembly, Collective Dynamics, and Autonomous Swimming**Abstract:**New physics and phenomena of how active structures interact with fluids have generated considerable excitement in the past decade. Uncovering physical mechanisms of the reciprocal dynamics in the biological/synthetic active systems often require developing ad-hoc theoretical models and simulation methods. In this talk, I will first discuss multiscale modeling and simulation of a bio-active synthetic fluid made from a microtubule/motor protein assembly. I will illustrate how the local particle-particle interactions lead to self-organization, and manifest themselves as large-scale collective motions due to a cascade of hydrodynamic instabilities. Furthermore, I will show that manipulation of active matter can be achieved by applying appropriate rigid or soft boundary confinements to guide them to perform useful mechanical work. Next, I will introduce a fictitious domain/active strain computation framework in simulation and design of bio-inspired soft robotic swimmers that can propel themselves in fluids by performing large deformations, as well as its applications for esolving a class of fluid-structure interaction problems in biomedical research.

### PICS Colloquium: Harish Vashishth (UNH)

- October 13, 2017

### PICS Colloquium: Weinan E (Princeton)

September 29, 2017

**Title**: “Deep learning, deep modeling and deep algorithms”**Abstract:**Deep learning has proven to be a powerful tool in computer vision and other tasks in artificial intelligence. It has also opened up new exciting possibilities in scientific modeling. I will discuss some of the applicationsof deep learning to molecular modeling and high dimensional partial differential equations.

### PICS Colloquium: Yves Dubief (University of Vermont)

April 21, 2017

**Title:**Role of Elasto-Inertial Turbulence in Maximum Drag Reduction**Abstract:**Minute concentrations of high molecular weight polymers have one exceptional and one intriguing properties in turbulent ows. Polymers show exceptional friction drag reduction capability (up to 80%). Yet, since the discovery of polymer drag reduction in 1949, the community has been intrigued by the asymptotic state called maximum drag reduction or MDR that appears to be universal to all polymer molecules tested so far. Whilst the mechanism of the polymer drag reduction is now considered understood , MDR remains somewhat a controversial topic. One school of thought argues that the dynamics of MDR is of Newtonian nature, i.e. the building blocks are the fundamental instabilities that create streaks and vortices as observed in turbulence with Newtonian uids. Support for this theory stems from numerical simulations using a specic approach to stabilize the polymer eld governing equation. This approach is the achilles’ heel of the claim that MDR is of Newtonian nature and an interesting tool to study MDR. Indeed this presentation will show that most simulations of polymer drag reduction have numerically ltered out a small scale phenomenon, called Elasto-Inertial Turbulence (EIT). EIT is a recently discovered new state of turbulence, where interactions between inertia and elastic eects can sustain a turbulence-like state in channel and pipe ows at Reynolds numbers much lower than the critical Reynolds at which Newtonian ows undergo a transition from laminar to turbulent state. The dynamics of EIT is an interesting and fairly unique inverse cascade system driven by the interplay of pressure, viscoelastic eects and velocity perturbations. EIT is found over a large range of Reynolds numbers from close to unity to well above critical, and not only in parallel, wall bounded shear ows but also in natural convection ows and surmised to exist in free shear ows. This presentation will introduce the second school of thought which argues that EIT is the cause of MDR. The relation between EIT and Elastic Turbulence (ET), a turbulent state discovered 17 years ago in inertia-less ows with curved streamlines, will also be discussed in the light of recent advances, in particular from Prof. Arratia, showing that ET may exist in parallel flows.

### PICS Colloquium: Christian Linder (Stanford)

February 10, 2017

**Title:**Beyond inf-sup: Stability estimates for multi-field variational principles by means of energetic conditions in incremental form**Abstract:**It is well known that mixed finite element methods have to satisfy certain criteria to provide solvability and stability. The latter criterion is, in the classical context of two-field saddle-point problems such as Stokes flow or quasi-incompressible elasticity, ensured by finite element types that satisfy the well-known inf-sup condition to ensure mesh-independent stability estimates. A number of finite element methods for novel multi-physics applications such as coupled Cahn-Hilliard-type flow in elastic media, extended phase-field models for fracture or topology optimization as well as gradient-extended plasticity models have a similar saddle-point structure. However, they correspond to a multi-field variational principle and only some of them suffer from similar instabilities. The question as to whether stability estimates are satisfied in these cases for standard discretizations and, if not, how conditions can be obtained that satisfy these estimates is particularly challenging.

### PICS Colloquium: Deyu Lu (Brookhaven National Laboratory)

November 18, 2016

First Principles Modeling of Electronic Excitations for Materials Applications**Title:**Electronic excitations are fundamental physical processes. Spectroscopic information, including absorption and emission spectra, from electron or photon probes is crucial for materials characterization and interrogation. When experimental data are supplemented and interpreted by first principles atomic modeling, a coherent physical picture can be established to provide physical insights into the intriguing structure-property-function relationship of functional materials. In this talk, the importance of the first principles modeling of electronic excitations is highlighted with three examples. In the first example, we investigated the oxygen 1s corelevel binding energy shift of bilayer silica films on Ru(0001) under different surface oxygen coverages in the X-ray photoelectron spectroscopy (XPS) measurement. Our study revealed that the binding energy shift is an electrostatic effect caused by the interplay of the surface and interface dipole moments. In the second example, we raised the question on an inverse problem: how to solve the underlying local structural arrangements from observed spectral features? As a proof of principle, we adopted ab intio X-ray absorption near edge structure (XANES) modeling for structural refinement of local environments around metal impurities of a gold nano cluster. In the third example, we are motivated to develop a local representation of the microscopic dielectric response function of valence electrons, which is a central physical quantity that captures the many-electron correlation effects. Although the response function is non-local by definition, a local representation in real space can provide insightful understanding of its chemical nature and improve the computational efficiency of first principles excited state methods. This research used resources of the Center for Functional Nanomaterials, which is a U.S.**Abstract:**

### PICS Colloquium: Arthi Jayaraman (University of Delaware)

October 28, 2016

Arthi Jayaraman, Associate Professor of Chemical & Biomolecular Engineering at the University of Delaware**Speaker:**Using Theory and Molecular Simulations to Link Molecular Design to Morphology and Function in Polymeric Materials**Title:**In my research group we develop molecular models, theory and simulation techniques to connect molecular features of macromolecular materials, specifically polymers, to their morphology and macroscopic properties, thereby guiding synthesis and characterization of these materials for various applications in the energy and biomedical fields. In the first part of my talk I will present our work on polymer functionalized nanoparticles containing polymer nanocomposites. The overarching goal of this work has been to control spatial arrangement of nanoparticles in the polymer matrix (i.e. polymer nanocomposite morphology) so as to engineer materials with target mechanical or optical properties. One way to tailor polymer nanocomposite morphology is by functionalizing nanoparticle surfaces with polymers, and systematically tuning the composition, chemistry, molecular weight and grafting density of these grafted polymers. We have developed an integrated self-consistent approach involving Polymer Reference Interaction Site Model (PRISM) theory and molecular simulations to study polymer grafted nanoparticles in polymer matrix, and to understand the effect of monomer chemistry, monomer sequence, and polydispersity, in the polymer functionalization on the effective interactions, and dispersion/assembly of functionalized nanoparticles in a polymer matrix. In this talk, I will present our recent results obtained using these computational techniques that agree with results from experiments by Prof. R. Krishnamoorti at University of Houston.**Abstract:**

### PICS Colloquium: Jeff Derby (University of Minnesota)

October 14, 2016

Jeff Derby, Distinguished McKnight University Professor of Chemical Engineering & Materials Science**Speaker:**Understanding multi-scale phenomena in bulk crystal growth processes via computational modeling: Pushing the continuum from the top down**Title:**From a modeling and simulation perspective, crystal growth processes represent some of the most complex and challenging systems ever analyzed, requiring a multidisciplinary and multi-scale approach and drawing on a wide range of scientific and engineering expertise. This lecture will describe continuum transport processes at play during crystal growth from a mathematical and computational modeling perspective.**Abstract:**

### PICS Symposium 2016: Emerging Paradigms in Scientific Discovery

October 6, 2016 to October 7, 2016

“Emerging Paradigms in Scientific Discovery”Find photos of the event here.**Title:**Please join us for a one and one-half day Symposium that focuses on emerging computational and mathematical approaches associated with discovering new mechanisms, constraints, conservation laws, and conceptualizing new theories amidst the landscape of Big Data. This theme touches upon diverse disciplines in health, materials science, biomedical science, marketing/business, and phenomics, which we broadly define as the scene of emergent properties or phenomena across disciplines.**Description:**

### PICS Colloquium: David Saintillan (UCSD)

September 23, 2016

David Saintillan, Associate Professor of Mechanical and Aerospace Engineering**Speaker:**Confining Active Fluids**Title:**Recent experimental studies have shown that confinement can profoundly affect self-organization in semi-dilute active suspensions, leading to striking features such as the formation of steady and spontaneous vortices in circular domains and the emergence of unidirectional pumping motions in periodic racetrack geometries. Motivated by these findings, we analyze the two-dimensional dynamics in confined suspensions of active self-propelled swimmers using a mean-field kinetic theory where conservation equations for the particle configurations are coupled to the forced Navier-Stokes equations for the self-generated fluid flow. In circular domains, a systematic exploration of the parameter space casts light on three distinct states: equilibrium with no flow, stable vortex, and chaotic motion, and the transitions between these are explained and predicted quantitatively using a linearized theory. In periodic racetracks, similar transitions from equilibrium to net pumping to traveling waves to chaos are observed in agreement with experimental observations and are also explained theoretically. Our results underscore the subtle effects of geometry on the morphology and dynamics of emerging patterns in active suspensions and pave the way for the control of active collective motion in microfluidic devices.**Abstract:**

### AMCS/PICS Colloquium Series: Dr. Jonathan Freund (UIUC)

April 22, 2016

**Speaker:**Dr. Jonathan Freund, Professor of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign**Title:**Cellular Blood Flow in Small Vessels

### AMCS/PICS Colloquium Series: Dr. Cameron Abrams (Drexel)

April 15, 2016

**Speaker:**Dr. Cameron Abrams, Professor of Chemical and Biological Engineering at Drexel University

### AMCS/PICS Colloquium Series: Dr. Jacob Fish (Columbia)

April 1, 2016

**Speaker:**Dr. Jacob Fish, Carleton Chaired Professor of Engineering at Columbia University**Title:**Computational Continua

### AMCS/PICS Colloquium Series: Dr. Gideon Simpson (Drexel)

March 25, 2016

**Speaker:**Dr. Gideon Simpson, Assistant Professor of Mathematics at Drexel University**Title:**Mathematical Formalisms for Molecular Dynamics

### AMCS/PICS Colloquium Series: Dr. Dimitris Maroudas (U Mass Amherst)

March 18, 2016

**Speaker:**Dr. Dimitris Maroudas, Professor of Chemical Engineering at the University of Massachusetts Amherst**Title:**Computational Studies of External-Field-Driven Surface Engineering and Optimal Design of Graphene-Based Nanomaterials

### AMCS/PICS Colloquium Series: Dr. Steve Shreve (Carnegie Mellon)

February 26, 2016

**Speaker:**Dr. Steve Shreve, Orion Hoch Professor of Mathematical Sciences at Carnegie Mellon University**Title:**A Diffusion Model for Limit-Order Book Evaluation

### AMCS/PICS Colloquium Series: Dr. William Massey (Princeton)

February 12, 2016

**Speaker:**Dr. William Massey, Edwin S. Wilsey Professor of Operations Research and Financial Engineering at Princeton University**Title:**Gaussian Skewness Approximation for Dynamic Rate Multi-Server Queues with Abandonment

### AMCS/PICS Colloquium Series: Dr. Hillel Aharoni (Penn)

January 29, 2016

**Speaker:**Dr. Hillel Aharoni, post-doctoral researcher at Penn**Title:**Geometry of Thin Nematic Elastomer Sheets

### AMCS/PICS Colloquium: Dr. Franck Vernerey (Boulder)

January 22, 2016

**Speaker:**Dr. Franck Vernerey, Associate Professor of Mechanical Engineering at the University of Colorado Boulder\**Title:**Computational Tissue Engineering: Tuning Tissue Growth with Scaffold Degradation in Enzyme-Sensitive Hydrogels

### AMCS/PICS Colloquium: Dr. Martin Ostoja-Starzewski (UIUC)

December 4, 2015

**Speaker:**Dr. Martin Ostoja-Starzeweski, Professor of Mechanical Science and Engineering at UIUC**Title:**Violations of Second Law of Thermodynamics vis-a-vis Continuum Mechanics

### AMCS/PICS Colloquium: Dr. Dennis Kochmann (CIT)

November 20, 2015

**Speaker:**Dr. Dennis Kochmann, Professor of Aerospace at California Institute of Technology**Title**: From Extreme Materials to Snapping Structures: Taking Advantage of Instability

### AMCS/PICS Colloquium: Dr. Jonathan Weare (University of Chicago)

November 13, 2015

**Speaker:**Dr. Jonathan Weare, Professor of Statitics at the University of Chicago**Title:**Understanding Stratification Approaches to Monte Carlo Simulation

### AMCS/PICS Colloquium Series: Dr. Tony Ladd (University of Florida)

November 6, 2015

**Speaker:**Dr. Tony Ladd, Professor of Chemical Engineering at University of Florida**Title:**Pattern Formation in Geological Systems

### AMCS/PICS Colloquium Series: Dr. Zhaosheng Yu (Zhejiang University)

October 30, 2015

**Speaker:**Dr Zhaosheng Yu, Professor of Mechancis at Zhejiang University**Title:**A Fictitious Domain Name for Fluid-structure Interactions

### AMCS/PICS Colloquium Series: Dr. Jin Feng (University of Kansas)

October 23, 2015

**Speaker:**Dr. Jin Feng, Professor of Mathematics at the University of Kansas**Title:**A Hamilton-Jacobi Formalism to Large Deviation and Associated Problems

### AMCS/PICS Colloquium Series: Dr. Srikanth Patala (NCSU)

October 16, 2015

**Speaker:**Dr. Srikanth Patala, Professor of Materials Science and Engineering at NCSU**Title:**Local Structural Analysis in Disordered Metallic Systems: Polyhedral Unit Models

### AMCS/PICS Colloquium: Dr. Nadia Heninger (Penn)

October 2, 2015

**Speaker:**Dr. Nadia Heninger, Professor of Computer and Information Science at Penn**Title**: Imperfect Forward Secrecy: How Diffie-Hellman Key Exchange Fails in Practice