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16.1.1 Petascale Science and Beyond: Applications and Opportunities for Materials, Chemical, and Bio Physics (lead: DCOMP, co-sponsors: DBIO/DCMP/DCP/DMP)
The United States, along with several countries around the world, are preparing to field exascale High Performance Computing (HPC) systems, capable of performing 1018 floating-point operations per second, in the next half decade. The US National Strategic Computing Initiative (NSCI) is currently executed within the Department of Energy (DOE) through the Exascale Computing Project (ECP) with the goal of fielding an exascale computer in 2021. This focus session will bring together researchers with experience in the effective utilization of high-performance data and compute infrastructure, including supercomputers, communication networks, and data resources to achieve breakthrough results. This includes researchers at experimental facilities such as light and neutron sources with extreme data-science requirements and researches in computational materials, computational chemistry and computational biophysics with experience in large scale simulations. We intend for this session to highlight strong examples of the state-of-the-art in computational science today leveraging large, national-scale infrastructure. The talks will highlight science results as well as opportunities and challenges in using energy-efficient, pre-exascale HPC systems to solve problems in materials, chemistry and biophysics.
16.1.2 Electrons, Phonons, Electron Phonon Scattering, and Phononics (lead: DCOMP, co-sponsor: DMP)
Electron-phonon interactions play a central role in many phenomena, most classically the resistivity of metals at ordinary temperatures, and are important for electrical and thermal conductivity of thermoelectrics, the temperature dependence of the optical band gaps of semiconductors, and other phenomena such as phonon drag. This focus topic covers electron-phonon interactions emphasizing fundamental physics, direct computation, first principles and phenomenological theory, optical and phonon spectroscopy and novel effects in nanostructures, nanodevices, 2D materials, and bulk materials. This focus topic also includes the emerging area of phononics, in particular manipulating phonon eigenstates, coherent superpositions and non-linearities, for example for logical operations or to manipulate sound or heat in unconventional ways.
Organizers: David J. Singh (University of Missouri) firstname.lastname@example.org, Matthieu Verstraete, (Universite de Liege) email@example.com, Baowen Li (University of Colorado) firstname.lastname@example.org.
16.1.3 First-principles Modeling of Excited-State Phenomena in Materials (lead: DCOMP, co-sponsors: DCMP/DCP/DMP)
Many properties of functional materials, interfaces, and nano-structures derive from electronic excitations. These processes determine properties such as ionization potential and electron affinity, optical spectra and exciton binding energies, electron-phonon coupling, charge transition levels, and energy level alignment at interfaces. In addition, hot carriers in semiconductors and nanostructures are generated, transition between excited states, transfer energy to the lattice, and recombine with each other. It is necessary to understand these properties from a fundamental point of view and to achieve design of materials with optimal performance for applications e.g., in transistors, light emitting diodes, solar cells, and photo-electrochemical cells. A proper description of electronic excitations requires theoretical approaches that go beyond ground state density functional theory (DFT). In recent years, Green’s function based many-body perturbation theory methods like RPA, GW, and BSE have been adopted by a rapidly growing community of researchers in the field of computational materials physics. These have now become the de facto standard for the description of excited electronic states in solids and their surfaces. Ehrenfest dynamics and surface-hopping schemes, e.g. based on time-dependent DFT, are used to describe coupled electron-ion dynamics as the origin of interesting physics in photo-catalysis, surface chemical reactions, scintillators, or radiation shielding. Advances in high performance computing and scalable implementations in several popular electronic structure packages enable further progress. Sophisticated calculations are accessible for many users and feasible for large, complex systems with up to few hundred atoms. These methods are increasingly applied to interpret experiments, such as spectroscopies and femto-second pump-probe measurements, and to computationally design functional materials, interfaces, and nano-structures. This focus topic is dedicated to recent advances in many-body perturbation theory and electron-ion dynamics methods for electronic excitations: challenges, scalable implementations in electronic structure codes, and applications to functional materials, interfaces, molecules, and nano-structures. It aims to attract researchers working on the nexus of electronic and optical properties of materials, hot electron dynamics, and device physics.
Organizers: André Schleife (University of Illinois at Urbana Champaign) email@example.com, Noa Marom (Carnegie Mellon University) firstname.lastname@example.org, Alexie Kolpak (MIT) email@example.com, Adrienn Ruzsinszky (Temple University) firstname.lastname@example.org.
16.1.4 Materials in Extremes: Bridging Simulation and Experiment (lead: DCOMP, co-sponsors: DMP/GSCCM)
The behavior of matter under extreme conditions of high pressure, temperature, strain and strain rate is of fundamental scientific importance. Geophysical processes in the core of the Earth and other planets, matter withstanding hypervelocity impacts of comets, shock wave compression of materials, detonation of explosives, high pressure and high temperature synthesis of novel materials, failure of materials reaching their intrinsic limit of performance, all require an understanding of the fundamental mechanisms of materials response at the atomic, microstructural, and continuum levels. Recent advances in theory and modeling, due to enormous increase in computer power combined with new computational techniques, have made it possible to extend simulations to the time and length scales of the experiments. This focus topic will assess recent experimental and computational efforts towards exploring the fundamental properties of materials at extreme conditions, including (1) high-pressure and high temperature synthesis and characterization of materials; (2) static and shock-induced materials behavior, including plasticity, phase transitions, and chemical reactions; (3) high strain rate phenomena occurring upon ultrafast energy deposition; (4) static and dynamic properties of energetic materials, including detonation phenomena; (5) properties of matter in the warm dense regime; and (6) new computational methods including development of interatomic potentials and multi-scale simulations.
Organizers: Ivan Oleynik (University of South Florida) email@example.com, Jonathan Belof (Lawrence Livermore National Lab) firstname.lastname@example.org, Anatoly Belonoshko (Royal Institute of Technology) email@example.com.
16.1.5 Precision Many-Body Physics (lead: DCOMP, co-sponsors: DAMOP/DCMP)
Precise understanding of strongly correlated materials and models is a major goal of modern physics. Achieving this understanding normally requires four complementary ingredients and thus four distinct directions of research: (i) conducting experiments that aim at producing highly accurate data, (ii) developing effective theories addressing the relevant degrees of freedom and/or emergent phenomena characteristic of a given phase of matter; (iii) solving simplified strongly correlated microscopic models either numerically or analytically, and (iv) cross-validating theoretical predictions against empirical data qualitatively and, ultimately, quantitatively. The last decade has seen breakthroughs made in all the four directions. An impressive progress has been achieved, and more is anticipated, where models and methods from many-body physics can be tested with precision, and where entirely new systems are realized that still await their accurate description. For example, in the field of ultra-cold atoms it is now feasible to perform analog quantum simulations aiming at experimental realization of key many-body quantum models and engineer novel Hamiltonians. Very recently, highly controllable experimental platforms also started to address fundamental questions about non-equilibrium quantum dynamics, opening the door to new dynamical phases of matter with no equilibrium counterpart. Given this background, this focus topic will bring together researchers who share the goal of achieving controllable theoretical and experimental understanding of phenomena taking place in correlated many-body systems. The key topics of the session(s) may include exactly solvable models and first-principles numeric approaches (such as tensor network and density-matrix renormalization group methods; path-integral, stochastic-series, and diagrammatic Monte Carlo techniques, dynamic cluster approximations, linked-cluster expansions, etc.); effective coarse-grained description of quantum phases and phase transitions; analytical and numerical methods yielding controllable description of topological phases (topological insulators, fractional quantum Hall states and Chern insulators, etc.), and precise experimental studies of strongly correlated bosonic, fermionic, and spin systems (both at and out of equilibrium).
16.1.6 Machine Learning for Many-Body Physics (lead: DCOMP, co-sponsor: DCMP)
Machine learning has emerged in recent years as a novel computational tool for studying a variety of physical systems. Condensed matter physics, while not the first field to take advantage of these techniques, is currently undergoing a machine learning revolution seeded by a number of pioneering works appearing in only the last few years. For example, machine learning has been used to find Green’s functions and density functionals of many-body systems. Artificial neural networks, as a subset of machine learning tools, have in particular shown great promise for classifying phases of matter realized in models in statistical mechanics, correlated electrons, topological matter, and frustrated magnetism. Neural networks have also been found to offer superior starting ansatze for variational techniques, used for solving the ground state of quantum many-body systems, representing topological states, performing many-body state tomography, and learning thermodynamic distributions useful for accelerating Monte Carlo simulations. Various dimension-reduction techniques have also been used to deduce phase transitions directly from high-dimensional configuration sets (such as spin states or atomic positions) for many-body systems. At a more fundamental level, important connections have been made between learning with neural networks, and conventional techniques in many-body physics such as the renormalization group and tensor networks. Finally, condensed matter experimentalists are beginning to harness the power of modern machine learning techniques in the analysis of very large data sets. The increasing number of preprints on arXiv.org provides clear evidence of the rapidly growing interest in machine learning not only in condensed matter, but also in related fields, such as statistical mechanics, and quantum information. This focus topic will cover a broad range of subjects ranging from applications of supervised and unsupervised machine learning to condensed matter physics, restricted Boltzmann machines and the expressive power of machine learning models for classical and quantum states, algorithm development, applications to quantum circuits and quantum error correction, and prospects in deep learning. In addition, connections to condensed matter experiments will be emphasized, as well as interdisciplinary connections to other fields in the physical sciences, such as quantum chemistry.
16.1.7 Advances in Computational Statistical Mechanics and their Applications (lead: DCOMP, co-sponsors: DCMP/GSNP)
Statistical mechanics is one of the foundations for the study of finite temperature properties and behaviors of physical systems and materials. In recent years, computer simulations have become increasingly indispensable to advance theoretical studies in these areas. Algorithms and methodologies continuously evolve to unleash the power of modern computers for attaining improved performance and accuracy, and for the study of more complex physical problems. This Focus topic aims to provide a platform to bring together researchers from different disciplines to discuss and showcase recent advancements in computational statistical mechanics, as well as their applications to frontier research problems. Relevant topics include (but are not limited to): simulation algorithms or techniques in computational statistical mechanics and their related studies; implementation techniques for modern computer architectures (e.g. GPUs or many-core processors); theoretical studies and discoveries aided or enhanced by computer simulations; applications of computational statistical mechanics to the study of thermodynamics, phase stability and transitions, critical phenomena at equilibrium, non-equilibrium, or irreversible processes for physical systems such as spin models, solid state systems, soft matter and biological systems.
16.1.8 Free Energy Mapping in Biology and Materials Science (lead: DCOMP, co-sponsors: DBIO/DMP/GSOFT)
Molecular simulations have long-held the promise of idealized experimental systems; an atomic-level microscope able to monitor and probe dynamics, thermodynamics and response at small length and timescales. One may, through judicious choice of molecular models and simulation conditions, explore the binding of biomolecules, the stretching of polymers, and the large scale thermodynamics of simple fluids and complex mixtures through both physical and alchemical means. A chief limitation these systems must overcome is one of sampling. Relative to experimental systems which may be monitored for days or more, making even relatively slow processes (on the order of seconds) relatively common, atomic-scale simulations are limited to probing systems on the order of microseconds. These events are essential when attempting to understand binding-unbinding events in drugs with nanomolar equilibrium constants and slow relaxation processes in composite materials or complex fluids. Simulations can be accelerated in one of two ways: (1) through coarse-graining a model into an efficient representation capable of achieving the same large-scale thermodynamic behaviors, or (2) through enhanced sampling methods, which accelerate the occurrence of slow processes to a computationally tractable timescale. This focus session seeks contributions from simulators at the cutting edge of method developments which speed up molecular simulations through algorithms rather than hardware, implementing exciting new sampling methods and applying them to diverse problems in biology and materials.
16.1.9 Computational Fluid Dynamics Across Length Scales: From Turbulence to Protein Folding and Aggregation (lead: DCOMP, co-sponsor: DFD)
Modern science and engineering is increasingly faced with problems dealing with complex states of flowing matter across a wide range of scales, from large-scale turbulence, to microfluidics, all the way down to biopolymer translocation across membranes or protein folding and aggregation in the cell, eventually further down into the quantum world. These inherently multiscale phenomena call for integrative approaches to computational modeling of flowing matter, i.e., methods that connect the four main levels of description of matter: i) continuum mechanics (Macro), ii) mesoscopic kinetic theory (Meso), iii) atomistic dynamics (Micro), and iv) quantum mechanics (Quantum). In the last three to four decades, each of the four levels of simulations have expanded on their own mainly owing to growth in computational power. Such an expansion has more recently been accompanied by efforts to develop a new generation of multiscale/multi-physics models designed to seamlessly couple these four levels of the hierarchy. Computational methods for flowing matter include a vast number of powerful techniques, such as finite-difference/volumes/elements for macro flows, mesoscale particle and lattice Boltzmann methods, molecular dynamics for nanoscale flows and a variety of quantum mechanical methods. Many of these techniques have begun to be coupled in the last two decades and progress has been made in enhancing the scope, efficiency, robustness, and user-friendliness of these multi-scale approaches. This focus topic will emphasize this progress, discuss the current state of the art in the field, and foster and promote new synergies and cooperation in the field of multi-scale modeling.