Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Development of High Performance Molecular Dynamics with Application to Multimillion-Atom Biomass Simulations

An understanding of the recalcitrance of plant biomass is important for efficient economic production of biofuel. Lignins are hydrophobic, branched polymers and form a residual barrier to effective hydrolysis of lignocellulosic biomass. Understanding lignin\u27s structure, dynamics and its interaction and binding to cellulose will help with finding more efficient ways to reduce its contribution to the recalcitrance. Molecular dynamics (MD) using the GROMACS software is employed to study these properties in atomic detail. Studying complex, realistic models of pretreated plant cell walls, requires simulations significantly larger than was possible before. The most challenging part of such large simulations is the computation of the electrostatic interaction. As a solution, the reaction-field (RF) method has been shown to give accurate results for lignocellulose systems, as well as good computational efficiency on leadership class supercomputers. The particle-mesh Ewald method has been improved by implementing 2D decomposition and thread level parallelization for molecules not accurately modeled by RF. Other scaling limiting computational components, such as the load balancing and memory requirements, were identified and addressed to allow such large scale simulations for the first time. This work was done with the help of modern software engineering principles, including code-review, continuous integration, and integrated development environments. These methods were adapted to the special requirements for scientific codes. Multiple simulations of lignocellulose were performed. The simulation presented primarily, explains the temperature-dependent structure and dynamics of individual softwood lignin polymers in aqueous solution. With decreasing temperature, the lignins are found to transition from mobile, extended to glassy, compact states. The low-temperature collapse is thermodynamically driven by the increase of the translational entropy and density fluctuations of water molecules removed from the hydration shell

Roadmap on multiscale materials modeling

Modeling and simulation is transforming modern materials science, becoming an important tool for the discovery of new materials and material phenomena, for gaining insight into the processes that govern materials behavior, and, increasingly, for quantitative predictions that can be used as part of a design tool in full partnership with experimental synthesis and characterization. Modeling and simulation is the essential bridge from good science to good engineering, spanning from fundamental understanding of materials behavior to deliberate design of new materials technologies leveraging new properties and processes. This Roadmap presents a broad overview of the extensive impact computational modeling has had in materials science in the past few decades, and offers focused perspectives on where the path forward lies as this rapidly expanding field evolves to meet the challenges of the next few decades. The Roadmap offers perspectives on advances within disciplines as diverse as phase field methods to model mesoscale behavior and molecular dynamics methods to deduce the fundamental atomic-scale dynamical processes governing materials response, to the challenges involved in the interdisciplinary research that tackles complex materials problems where the governing phenomena span different scales of materials behavior requiring multiscale approaches. The shift from understanding fundamental materials behavior to development of quantitative approaches to explain and predict experimental observations requires advances in the methods and practice in simulations for reproducibility and reliability, and interacting with a computational ecosystem that integrates new theory development, innovative applications, and an increasingly integrated software and computational infrastructure that takes advantage of the increasingly powerful computational methods and computing hardware

Simulation modelling and visualisation: toolkits for building artificial worlds

Simulations users at all levels make heavy use of compute resources to drive computational simulations for greatly varying applications areas of research using different simulation paradigms. Simulations are implemented in many software forms, ranging from highly standardised and general models that run in proprietary software packages to ad hoc hand-crafted simulations codes for very specific applications. Visualisation of the workings or results of a simulation is another highly valuable capability for simulation developers and practitioners. There are many different software libraries and methods available for creating a visualisation layer for simulations, and it is often a difficult and time-consuming process to assemble a toolkit of these libraries and other resources that best suits a particular simulation model. We present here a break-down of the main simulation paradigms, and discuss differing toolkits and approaches that different researchers have taken to tackle coupled simulation and visualisation in each paradigm

Molecular Dynamics Studies of Liquid and Chain Systems

Molecular Dynamics simulation has been used for the past 20 - 30 years to study interfacial properties of liquids though the foundations for these studies were laid as far back as 1791 when the astronomer Joseph Dalambre used the time reversible algorithm, commonly called the Verlet algorithm, for the integration of Newton's equations. Some of the properties obtained from Molecular Dynamics, commonly called MD, simulation are density profiles, system configurations, as well as stress or pressure tensor profiles. Generally, the surface tension has been calculated by integrating the stress tensor profile over the width of the interfacial region. In an effort to circumvent the stress tensor calculation and the technical difficulties associated with extensions to include many-body interactions, I will study the feasibility of implementing an equality recently developed by C. Jarzynski to determine the equilibrium surface free energy and, subsequently, the surface tension of an immiscible L-J fluid from an ensemble average of a set of non-equilibrium simulations. In addition to exploring suitable systems for this study, we explore relative computational efficiency of the second method. We also compare the equilibrium free energy difference computed by the Jarzynski method to the apparent free energy difference computed by the Irving-Kirkwood (IK1) approach. We conclude first that both the Jarzynski and IK1 approaches can be useful tools in simulating immiscible liquid systems. The Jarzynski relation is quite effective at extracting free energy differences associated with interfacial area changes in systems comprised of closely spaced, interacting interfaces. For isolated interfaces, the IK1 method is still the best approach for obtaining interfacial tension. We also find that a fast switching Jarzynski algorithm is as efficient and much less costly to implement than a slow switching method

Modelling solid/fluid interactions in hydrodynamic flows: a hybrid multiscale approach

With the advent of high performance computing (HPC), we can simulate nature at time and length scales that we could only dream of a few decades ago. Through the development of theory and numerical methods in the last fifty years, we have at our disposal a plethora of mathematical and computational tools to make powerful predictions about the world which surrounds us. From quantum methods like Density Functional Theory (DFT); going through atomistic methods such as Molecular Dynamics (MD) and Monte Carlo (MC), right up to more traditional macroscopic techniques based on Partial Differential Equations (PDEs) discretization like the Finite Element Method (FEM) or Finite Volume Method (FVM), which are respectively, the foundation of computational Structural Analysis and Computational Fluid Dynamics (CFD). Many modern scientific computing challenges in physics stem from combining appropriately two or more of these methods, in order to tackle problems that could not be solved otherwise using just one of them alone. This is known as multi-scale modeling, which aims to achieve a trade-off between computational cost and accuracy by combining two or more physical models at different scales. In this work, a multi-scale domain decomposition technique based on coupling MD and CFD methods, has been developed to make affordable the study of slip and friction, with atomistic detail, at length scales otherwise impossible by fully atomistic methods alone. A software framework has been developed to facilitate the execution of this particular kind of simulations on HPC clusters. This have been possible by employing the in-house developed CPL_LIBRARY software library, which provides key functionality to implement coupling through domain decomposition.Open Acces

Efficient implementation of atom-density representations

Physically motivated and mathematically robust atom-centered representations of molecular structures are key to the success of modern atomistic machine learning. They lie at the foundation of a wide range of methods to predict the properties of both materials and molecules and to explore and visualize their chemical structures and compositions. Recently, it has become clear that many of the most effective representations share a fundamental formal connection. They can all be expressed as a discretization of n-body correlation functions of the local atom density, suggesting the opportunity of standardizing and, more importantly, optimizing their evaluation. We present an implementation, named librascal, whose modular design lends itself both to developing refinements to the density-based formalism and to rapid prototyping for new developments of rotationally equivariant atomistic representations. As an example, we discuss smooth overlap of atomic position (SOAP) features, perhaps the most widely used member of this family of representations, to show how the expansion of the local density can be optimized for any choice of radial basis sets. We discuss the representation in the context of a kernel ridge regression model, commonly used with SOAP features, and analyze how the computational effort scales for each of the individual steps of the calculation. By applying data reduction techniques in feature space, we show how to reduce the total computational cost by a factor of up to 4 without affecting the model’s symmetry properties and without significantly impacting its accuracy

Enhanced droplet spreading due to thermal fluctuations

The dynamics of thin film liquid interfaces (< 100 nm) play dominant roles in many macroscale phenomena, such as droplet break up, evaporation of a meniscus, boiling and heat transfer, and thin film dynamics. These processes are can be used in the manufacturing of microelectronics and biomedical devices. Unlike for thicker films, the thermal motion of the constitutive molecules can affect dynamics and may need to be included when modeling nanometer mechanical systems. Yet, explicitly modeling all of the molecules of a thin liquid film interface is often intractable. Thus there is a need for simplified continuum models that still contain the relevant physics of thin films. Theoretical methods do exist for including thermal energy into continuum equations; however experimental verification of these methods is still unavailable, primarily due to the complications of measuring dynamical quantities at the nanometer length scales. This dissertation focuses upon the study of molecular films via molecular dynamics simulations in order to assess the relevant physics needed for continuum modeling of thermally perturbed fluid flows. The first project of my dissertation compared the continuum predictions of capillary wave relaxation in thin fluid film interfaces to the behavior of molecular simulations. We found that for all but the smallest length scales in the free-fluid interface, continuum predictions matched that of the molecular simulations for the both the amplitude and the decay rates of the thermal capillary waves. However, at smaller length scales, there also existed a transition in behavior where the wave amplitudes of the continuum model and the molecular simulations matched, but the decay rates of waves in the molecular simulations adopted a new, molecular-size-dependent decay behavior. Expanding upon the success of the continuum modeling for all but the smallest length scales of thermally perturbed thin liquid films, in this work we study the spreading of a large molecular drop on a solid plate and test whether thermal forces can dominate spreading dynamics as predicted by Davidovitch et al.. By comparing the spreading of a simulated molecular drop, we not only find an enhanced spreading rate, but also find that the spreading dynamics found well matched the predictions of a thermally augmented continuum model