16.01.01 Matter in Extreme Environments (DCOMP, DMP)
This session will focus on the behavior of matter under extreme conditions of pressure, strain, compression and impacts, often in combination with extreme electromagnetic fields and particle irradiation. The session will bring together theoreticians and experimentalists from a broad range of fields including physics, chemistry, materials science, as well as earth and planetary science and astrophysics for discussions in areas that include, but are not limited to:
- New quantum behavior found at extremes such as superconductivity with critical temperatures approaching room temperature.
- New theoretical and computational techniques including methods for crystal structure prediction, improved treatment of electronic structure and properties calculations, as well as the development of algorithms to advance dynamic and multi-scale simulations.
- The behavior of matter in strong magnetic and electric fields.
- The development of novel experimental techniques and diagnostics.
- High pressure and high temperature synthesis of novel materials.
- Materials behavior under static and dynamic compression including phase transitions, equations of states, chemical reactivity and the emergence of novel properties.
- Warm dense matter.
- Novel materials, including: energetic, superhard, quantum, earth and planetary science inspired.
- Melting, and the behavior of liquids under extreme conditions.
- Biophysics at extreme conditions, including deep life.
Eva Zurek, Koichiro Umemoto, Andreas Hermann, Gilbert (Rip) Collins, Shanti Deemyad, Antonio M. dos Santos
16.01.02 Building the Bridge to Exascale: Applications and Opportunities for Materials, Chemistry, and Biology (DCOMP, DAMOP, DBIO, DCP, DMP, DPOLY) [same as 01.01.48, 4.01.24, 05.01.13, 06.01.08]
High Performance Computing (HPC) plays a critical role in modern scientific discovery through a merging of simulation, modeling and experimental data analysis. HPC facilities around the world, are preparing to field exascale HPC systems, capable of performing more than 1018 floating-point operations per second, in the next several years. For example, the U.S. National Strategic Computing Initiative (NSCI) is currently underway with the goal of fielding exascale computers in 2021. The advent of exascale computing brings with it both tremendous opportunity for scientific discovery as well as challenges for harnessing this technology in scientific applications. This focus session will bring together researchers with experience in using high-performance cyberinfrastructure, including supercomputers, communication networks, and data resources, to achieve breakthrough scientific results in materials, biological, and chemical physics. This includes researchers at experimental facilities such as light and neutron sources with extreme data-science requirements, including machine-learning approaches, and researchers in computational materials, computational chemistry and computational biophysics with experience in large-scale simulations. Software-development projects preparing a variety of physics applications for exascale-class machines will also be presented. This session will highlight forefront examples of the state-of-the-art in computational physics today leveraging large, national-scale infrastructure.
Jack C. Wells, Jack R. Deslippe, Anouar Benali
16.01.03 Electrons, Phonons, Electron Phonon Scattering, and Phononics (DCOMP, 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 or topological acoustic materials, including active materials.
David J. Singh, Ivana Savic, Matthieu Verstraete, Xiulin Ruan
16.01.04 First-principles modeling of excited-state phenomena in materials (DCOMP, DCP, DMP) [same as 05.01.14]
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.
Yuan Ping, Sahar Sharifzadeh
, Feliciano Giustino, Serdar Ogut
16.01.05 Machine Learning for Quantum Matter (DCOMP, DMP, GDS) [same as 23.01.10]
Quantum matter, the research field studying states of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, quantum information, quantum gravity, and large-scale numerical simulations. Recently, the condensed matter, quantum information, and atomic, molecular, and optical physics communities have turned their attention to the algorithms underlying modern machine learning, with an eye on making progress in quantum matter research. This has led to several breakthroughs where machine learning algorithms recognize conventional and topological states of matter, including the revelation of phases of matter previously unidentified by conventional techniques in disordered spin chains, as well as the application of machine learning algorithms to spot hidden order in images of a bizarre state in high-temperature superconductors. As evidenced by the community embracing a wide array of activities related to research at the intersection of machine learning and quantum mechanics, as well as the continuous appearance of increasingly creative research activity in this area, it is clear that over the next few years, machine learning will become very important for the computational study of condensed matter, quantum information, and other areas of quantum physics. This focus topic includes, but is not limited to, topics such as machine learning many-body systems, machine learning for materials and experimental data, machine learning quantum states, and materials design and discovery.
Juan Felipe Carrasquilla Alvarez, Giacomo Torlai, William Ratcliff, Ehsan Khatami
16.01.06 Precision Many-Body Physics (DCOMP, DAMOP, DCMP) [same as 06.01.05]
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. Controllable experimental platforms also started to address fundamental questions about non-equilibrium quantum dynamics, discovering new dynamical phases of matter with no equilibrium counterpart. The focus sessions that 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 sessions may include exactly solvable models, dualities and correspondences between seemingly unrelated theories (enabling the transfer of results and ideas), 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 for topological phases (including quantum spin-liquids, 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).
Nikolay Prokofiev (University of Massachusetts, Amherst), Tigran Sedrakyan (University of Massachusetts, Amherst), Ian Spielman (University of Maryland), Kristjan Haule (Rutgers University)
16.01.07 Understanding Glasses and Disordered Matter Through Computational Models (DCOMP, DPOLY, DSOFT, GSNP) [same as 01.01.49, 02.01.70, 03.01.27]
Our ever-expanding ability to computationally interrogate idealized yet complex mathematical descriptions of disordered matter such as glasses, poly-disperse colloidal aggregates, amorphous polymers, and granular packings has provided novel means to understand their underlying physics. Oftentimes surprisingly robust structural features can be discerned to arise in systems that otherwise seem to be entirely random by nature. Concepts related to energy landscapes, polymer entanglement and non-equilibrium thermodynamics are some of the areas that have been elucidated by carefully constructed computational investigations. New algorithms for optimizing and exploring disordered systems also continue to emerge, providing tools for addressing these largely under-studied yet ubiquitous materials systems. This session will discuss applications of established computational methods toward the study of these systems as well as development of novel algorithms for their further elucidation.
Michael Falk, Pengfei Guan, M. Lisa Manning, Joerg Rottler
16.01.08 Computational Methods for Statistical Mechanics: Advances and Applications (DCOMP, GSNP) [same as 03.01.26]
Systems with a large number of degrees of freedom are fundamental for describing macroscopic behavior in a wide area of physical sciences and beyond. Consequently, statistical mechanics is one of the foundational theories for describing systems with disorder, limited microscopic knowledge and at finite temperature. Computer simulations are indispensable to advance understanding in these areas. In conjunction with modern computer architectures, new and improved algorithms and methodologies enable increased computational performance and accuracy and the study of more complex physical problems. The main focus of this session will be on new methods and capabilities of Monte-Carlo, Molecular-Dynamics and Spin-Dynamics methods and their combinations. This Focus Session aims to provide a platform to bring together researchers from different disciplines to discuss and showcase recent advancements in computational statistical physics, as well as their applications to research problems at the frontier of computational physics.
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, disorder driven phenomena, non-equilibrium, or irreversible processes for physical systems such as spin models, solid state systems, polymers and biological systems.
Markus Eisenbach, Ying Wai Li, David P. Landau
16.01.09 Real Space Methods for the Electronic Structure Problem: New Algorithms and Applications (DCOMP)
Many interesting material properties can be understood and predicted by computation involving a solution of the electronic structure problem. The combination of new algorithms applied to high performance computing platforms promises a number of potential advances in the understanding of the theory of complex materials and in the analysis of new experimental work on advanced materials. Yet, solution of the electronic structure problem remains computationally challenging when the system of interest contains a large number of atoms.
Real-space numerical electronic structure methods are mathematically robust, accurate and ideally suited for contemporary massively parallel computational resources. Real space methods have successfully been applied to both ground state and excited states, especially but not only for localized systems such as nanoscale clusters. New algorithms have been developed to optimize solutions to eigenvalue problems and expedite or circumvent the computation of empty states in excited state computations.
Topics in this focus session include but are not limited to: real space or grid based methods using finite differencing, finite elements, or variations thereof; applications to large nanoscale systems, ab initio molecular dynamics, noncollinear magnetic systems, optical excitations, and molecular transport; new algorithms designed for expediting and applying these methods to state of the art computational platforms.
Jim Chelikowsky, Leeor Kronik, Angel Rubio
16.01.11 Emerging Trends in Molecular Dynamics Simulations and Machine Learning (DCOMP, DPOLY, DSOFT, GDS) [same as 01.01.50, 02.01.71, 23.01.12]
Recent advances in force fields, algorithm design, data analytics, and large scale parallel machines have stimulated great interest in modeling hard and soft materials and biological systems with molecular dynamics (MD) simulations. Multimillion-to-billion atom MD simulations with classical force fields trained by ab initio quantum mechanical (QM) simulations can reliably describe charge transfer, bond breaking/bond formation, and chemical reactions in materials under normal and extreme operating conditions. Machine learning approaches are greatly accelerating the development of quantum-mechanically informed force fields for massively parallel MD simulations. Combining coarse grained and atomistic modeling with machine learning methods enable high-throughput screening of materials. Accelerated dynamics approaches have enabled MD simulations to reach sufficiently long time scales to study rare events.
The focus session will cover a wide range of topics that include but are not limited to:
- Force field development with machine learning approaches
- On-the-fly coarse and fine graining of MD simulations
- Accelerated dynamics methods
- Data analytics using neural networks
- Peta-to-exascale algorithms for long-range interactions
Priya Vashishta, Roberto Car, Gary S. Grest, Brian Barnes
16.01.12 Modeling the electrochemical interface and aqueous solutions (DCOMP, DCP) [same as 05.01.15]
This focus topic covers the field of computational modeling of solid/aqueous-electrolyte interfaces. It includes but is not limited to understanding the need to accurately describe electronic charge transfer and localization in solvated ionic environments, the importance of many-body effects and non-local correlations, the role of polarization in simulations of electrolyte solutions, the level alignment at the electrochemical interface and wet semiconducting surfaces, etc. The topic is also open to experimentalists interested on understanding the fundamentals of these systems at the atomistic level.
Marivi Fernandez-Serra, Luana Pedroza, Alexandre Reily Rocha
16.01.13 Physics and effects on transport of ion-ion correlation in electrolyte materials (DCOMP, DCP, DMP) [same as 05.01.16]
This focus session is motivated by the surge of interest over the last five years in concentrated electrolyte materials for high-energy and high-capacity batteries. As part of the strategy to increase the battery power density and safety, materials composed of high concentrations of alkali-salt in ionic liquids and solid amorphous polymers are increasingly seen as attractive candidates to replace conventional organic electrolytes. Important technological parameters such as charge/discharge rates and efficiency of the whole battery are controlled and underpinned by the electrolyte transport properties, namely ionic conductivity and transference number. The ionic nature and high concentrations at play clearly point at the adoption of rigorous concentrated solution theory for both experimental and computational efforts dedicated at understanding the complex transport dynamics in such materials. The past year has seen a tremendous increase in interest around correlated ionic transport properties, and new effects like a negative transference number have been uncovered. Contributions to this focus session will include both computational and experimental investigations of correlated ionic transport in electrolyte materials.
Arthur France-Lanord, Nicola Molinari
16.01.14 van der Waals Interactions in Molecules, Materials, and Complex Environments (DCOMP)
van der Waals (vdW) interactions play a central role in determining the stability, structure, and function of systems throughout biology, chemistry, physics, and materials science. Arising from coupled instantaneous charge fluctuations in matter, these non-bonded interactions are quantum mechanical in nature and therefore pose a substantial challenge to both theory and experiment. This focus session will cover state-of-the-art theoretical and experimental approaches for gaining a fundamental understanding of vdW interactions in a range of systems spanning molecules (e.g., intra- and inter-molecular vdW interactions, molecular dimers and clusters), materials (e.g., molecular crystals, insulators, semiconductors, metals, surfaces), as well as complex environments (e.g., in the presence of solvent, electromagnetic fields, finite thermodynamic conditions).
Robert A. DiStasio Jr., Noa Marom, Matthias Scheffler
16.01.15 Heat transport in condensed systems: ballistic, hydrodynamic, diffusive, and quantum (DCOMP)
Thermal transport plays a key role in many phenomena in condensed and low-dimensional systems, and has vital implications in thermoelectric applications. Heat management is also a key technological challenge in electronic, photonic, and solar cell devices. Recent advances in materials science and device fabrication offer new opportunities for an interplay of electronic and phononic degrees of freedom, leading to complex device cooling pathways. Low-dimensional materials exhibit unique hydrodynamics often distinct from that observed in typical three-dimensional materials. Additionally, while vibrations can be considered thermal noise, single phonons could be used to carry quantum information, offering new paradigms in quantum information science and technology. Corresponding recent developments in modeling and simulation provide new opportunities to gain fundamental insights to these emerging behaviors.
This session will focus on thermal transport in the diffusive, hydrodynamic, and ballistic regimes, on interfacial thermal resistance, on quantum phononics, and on first-principles and molecular dynamics simulations of thermal conductivity and phonon drag in nanostructures, 2D materials, and bulk materials.
Vasili Perebeinos, Elif Ertekin, Nicola Marzari