Focus Topics

DCOMP Lead Focus Topics

APS March Meeting 2023

16.01.02 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

Department of Physics and Astronomy

University of Missouri


Elena Roxana Margine

Applied Physics and Astronomy

Binghamton University, SUNY


Ivana Savic

Tyndall National Institute, Cork, Ireland


Xiulin Ruan

School of Mechanical Engineering

Purdue University



16.01.03 First principles modeling of excited-state phenomena in materials (DCOMP, DCP, DMP)

Many properties of functional materials, including bulk and two-dimensional materials and their
interfaces, as well as quantum and topological materials, derive from excited-state phenomena.
These processes determine properties such as band gaps, excitonic effects, electron-phonon
couplings, and out-of-equilibrium dynamics of charge, spin, orbital, and lattice degrees of freedom
and their couplings. Gaining deeper insight into these properties will advance our fundamental
understanding of electronic structure theory beyond ground-state phenomena and will underpin
the design of new and improved materials for applications in energy-efficient electronics, solid-
state lighting, solar photovoltaics, photocatalysis, and quantum technologies.

Predictive calculations of electronic excitations require theoretical frameworks that go beyond the
ground-state density functional theory (DFT). In recent years, Green’s function based many-body
perturbation theory methods like RPA, GW, BSE and beyond GW/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. Nonequilibrium
Green’s Function methods with many-body interactions from first principles can be promising to
tackle complex ultrafast and out-of-equilibrium quantum dynamics of excitons, electrons, phonons
and spin. The description of electron-phonon and spin-phonon interactions using many-body
Green’s function and density-matrix methods is also becoming increasingly popular.

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 a few hundred atoms. Coupling
with machine learning methods, the computational cost of excited state calculations can be further
lowered by orders of magnitude. 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 the theory
of electron-phonon interactions in the excited state: challenges, scalable implementations in
electronic structure codes, new machine learning and data-driven approaches, and applications to
functional materials, two-dimensional materials and their interfaces, and quantum and topological
materials. It aims to attract researchers working on the nexus of electronic and optical properties
of materials, electron-phonon interactions, as well as materials and device physics.


Yuan Ping (co-lead)

Department of Chemistry and Biochemistry

University of California, Santa Cruz

Santa Cruz, CA 95060

Phone: 831-459-1390



Feliciano Giustino (co-lead)

Oden Institute for Computational Engineering and Sciences and Department of Physics

University of Texas at Austin

Austin TX 78712

Phone: 512-232-5755



Sohrab Ismail-Beigi

Department of Applied Physics

Yale University

New Haven CT 06520

Phone: 203-432-2107



Li Yang

Department of Physics

Washington University

Saint Louis MO 63131

Phone: 314-935-9453




16.01.04 Machine learning for quantum matter (DCOMP, GDS, DMP)

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, condensed matter, quantum information, material science, 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 in which machine learning algorithms are used, for example, to study large-scale materials with unprecedented accuracy. Other examples concern the ab-initio simulation of electronic structure problems, automated design of experiments, and the optimization of parameterized quantum circuits.  As evidenced by the community embracing a wide array of activities related to research at the intersection of machine learning and quantum mechanics (e.g. KITP program on Machine Learning for Quantum Many-Body Physics, Machine Learning for Physics and the Physics of Learning at IPAM/UCLA, Machine learning for quantum design at Perimeter Institute, Machine Learning for Quantum Simulation at Flatiron Institute, ELLIS Quantum Machine Learning, NEURIPS workshop on machine learning and the physical sciences), 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.


Evert van Nieuwenburg

Leiden University, Netherland


Filippo Vicentini
Collaborateur Scientific 
EPFL, Lausanne, Switzerland



16.01.05 Precision Many-Body Physics (DCOMP, 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) de-veloping 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 mod-els either numerically or analytically, and (iv) cross-validating theoretical predictions against empiri-cal data qualitatively and, ultimately, quantitatively. The last decade has seen breakthroughs made in all 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 the 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 proposal is to organize 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 session(s) may include exactly solvable models, dualities, and correspondences be-tween seemingly unrelated theories (enabling the transfer of results and ideas), first-principles numer-ic approaches (such as tensor network and density-matrix renormalization group methods; path-integral, stochastic-series, and diagrammatic Monte Carlo techniques, dynamic cluster approxima-tions, 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). 


Tigran Sedrakyan

Department of Physics

University of Massachusetts, Amherst


Boris Svistunov

Department of Physics

University of Massachusetts, Amherst


Zubin Jacob

Purdue University


16.01.06 Understanding Amorphous Matter Through Modeling and Simulation (DCOMP, DSOFT)

Simulations of complex models of disordered matter such as glasses, poly-disperse colloidal aggregates, amorphous polymers, dense suspensions, and granular packings have provided much insight into their underlying physics. Surprisingly, robust structural features can be discerned to arise in systems that otherwise seem to be entirely random by nature. Concepts related to non-equilibrium thermodynamics, energy landscapes, polymer entanglement are some examples that have been elucidated by carefully constructed computational investigations. New algorithms for optimizing and exploring structural, mechanical, rheological properties of disordered systems continue to emerge, providing tools for addressing these 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 that hold the promise of overcoming limitations of current simulation strategies.


Aniket Bhattacharya

Department of Physics

University of Central Florida


Robert Riggleman

Chemical and Biomolecular Engineering Department

University of Pennsylvania



16.01.07 Computational methods for statistical mechanics: advances and applications (DCOMP, GSNP)

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 are being developed to 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 application. 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

Advanced Computing for Chemistry and Materials
Center for Computational Sciences
Oak Ridge National Laboratory


16.01.08 Recent applications and developments in quantum embedding methods (DCOMP)

The problem of treating systems of strongly interacting electrons is a key fundamental problem in condensed-matter physics and quantum chemistry, as such interactions govern the behavior of many promising classes of functional materials and molecules. Quantum embedding methods, in which a strongly interacting active space is embedded in a weaker correlated environment, has proven to be a powerful approach for treating such systems. Embedding methods have been developed for a wide range of correlated electron problems, ranging from simplified models to realistic bulk materials/molecules. In addition, embedding methods are well suited for inhomogeneous systems such as bulk or surface impurities in solid-state hosts.

Under the general approach of quantum embedding, there is a variety of methods with significant recent developments including those based on the electronic density, e.g., density-functional embedding; Green’s functions, e.g., dynamical mean-field theory (DMFT); and the density matrix, e.g., density-matrix embedding theory (DMET). A key research area in terms of the methodology is the development of robust, accurate, and efficient many-body solvers based on, e.g., quantum Monte Carlo techniques, exact diagonalization, renormalization group approaches, or quantum chemistry methods. In addition, there has been considerable efforts to expand the physics that can be treated by embedding methods to include, e.g., out-of-equilibrium phenomena and two-particle response functions. Another active area of research when embedding methods are applied to complex materials and molecules deals with the approach for systematically obtaining and parametrizing the subset of electronic states to act as the low-energy active space (i.e., “downfolding”), and merging many-body methods with density-functional theory (DFT). Recent work has focused on many-body perturbation theory such as GW to treat the environment, for a more well-defined connection between many-body methods in DMFT or DMET. Finally, there has been significant recent work on “full cell” embedding, in which the active space includes the full electronic structure of a unit cell or supercell of a bulk material. This focus session welcomes abstracts in the above-mentioned areas and beyond.


Cyrus Dreyer
Department of Physics and Astronomy
Stony Brook University
Stony Brook, NY 11794-3800


16.01.09 Emerging trends in molecular dynamics simulations and machine learning (DCOMP, GDS, DSOFT, DPOLY)

Recent advances in deep learning has created a mini-revolution in all areas of Physics. Deep Learning (DL) has accelerated determination of complex energy landscapes, and stimulated algorithm design and data analytics. Availability of exascale machines within 1-2 years will make it easier to model hard and soft materials and biological systems with deep learning in conjunction with molecular dynamics (MD) simulations. Multimillion-to-billion atom MD simulations with DL trained on ab initio quantum mechanical simulations can reliably describe charge transfer, bond breaking/bond formation, and chemical reactions in materials under normal and extreme operating conditions. Generative models such as variational autoencoder (VAE) and generative adversarial networks (GAN) are very powerful DL models and have shown great success in creating material atomic structure for a desired property. Reinforcement learning (RL) is another widely used technique in DL domain, which is more suitable for these problems involving an optimization task and a sequential decision making under uncertainty. For examples, RL models have been used to predict reaction pathways, optimal conditions for chemical reactions and even design polymers with desired physical properties. Combining coarse grained and atomistic modeling with DL methods enable high throughput screening of materials. Accelerated dynamics approaches have enabled MD simulations to reach sufficiently long-time scales to study rare events. Novel task parallel frameworks are emerging for the analysis of peta-to-exascale DL-MD simulations.

Invited and contributed presentations in focus sessions will cover a wide range of topics that include but are not limited to:

·        Deep learning for energy landscapes and force field development

·        On-the-fly coarse and fine graining of MD simulations

·        Accelerated dynamics using reinforcement learning

·        Materials design using VAE and GAN

·        Peta-to-exascale algorithms for hybrid deep learning and MD simulations



Priya D Vashishta

Viterbi School of Engineering

University of Southern California


Rajiv Kalia

Viterbi School of Engineering

University of Southern California


16.01.10 Modeling the electrochemical interface (DCOMP)

This session will focus on understanding the microscopic mechanisms that drive the functionality of electrochemical and photoelectro-chemical interfaces. These include metallic and semiconducting interfaces under bias conditions.

Specific topics related to the above interfaces include: electro-catalysis, photocatalysis, electrochemistry and ionic transport. Abstracts are also invited on application of machine learning approaches to address the above problems. The session will welcome talks that focus both on method development and applications, including the search for more efficient functional interfaces.


Marivi Fernandez-Serra
Department of Physics & Astronomy

Stony Brook University.
Stony Brook, NY 11794-3800



16.01.11 Extreme-Scale Computational Science Discovery in Fluid Dynamics and Related Disciplines (DCOMP, DFD)

Computational Physics is a broad, interdisciplinary field of research, which involves the development of computational techniques and the analysis of large datasets combined with theory, experiments and modeling, in concert with the so-called domain sciences. One important and itself broad field of science represented by APS is fluid dynamics, in which researchers have long embraced scientific computing based on partial differential equations. While fully resolved solutions of most practical flow problems are still not feasible, researchers have made use of the largest computing platforms available (including those at or near the Exascale) to help advance scientific discovery and improve the engineering models needed in applications. The recent advancements in physics informed machine learning algorithms also have the potential to significantly speed-up the model improvements. This focused section will cover three main themes: (a) Progress in extreme-scale HPC techniques for fluid flow problems. (b) Challenges in enabling access to massive data sets for non-HPC researchers, and (c) Machine learning, artificial intelligence and data analytics as applied to fluid dynamics.


P. K. Yeung

Department of Aerospace Engineering

Georgia Institute of Technology

Daniel Livescu

Computational and Statistical Sciences Division,

Los Alamos National Laboratory

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