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APS March Meeting 2023

March 5–10, 2023 | Las Vegas, Nevada
March 20–22, 2023 | Virtual

DPOLY Symposium Invited Speaker (Invitation Only)

Invitation Only

Data-analytic techniques, such as machine learning, are accelerating development of materials classification, predictions of new properties or formulations, data extraction from natural language processing, and design and analyses of experimental assays. We solicit contributions that intertwine polymers and soft materials with machine learning and data. Of particular interest are autonomous experiments, efforts unifying data science with experimental research, work on generating high-throughput data collection strategies, developing high-throughput instrumentations and characterization techniques, explorations of vast existing datasets for the design of new materials, development of highly efficient simulations, and techniques to mine or combine data of different forms and different sources (e.g. simulations, rheology or scattering) that elucidate fundamental polymer physics.

Machine Learning, Autonomous Experiments, and Big Data in Polymer Physics

This session invites work concerning organic electronics (including but not limited to: the physics that govern conjugated polymers, charge transport in macromolecular systems, mixed ionic and electronic conductors, redox-active macromolecules, and efforts dealing with the polymer physics of organic electronic devices) as well as organic photonics (including but not limited to: the physics that govern interactions between electromagnetic waves and soft materials, light propagation in macromolecular systems, photonic band gaps of soft materials, and microphase-separated/ordered soft materials for photonics applications). Experimental, computational, and theoretical studies are encouraged. 

Organic Electronics and Photonics

We live in a material world where nonequilibrium phenomena are the dominant theme of the day, and polymers are no exception. This focus session aims to bring together both experimental and theoretical researchers to discuss recent advances in the understanding of nonequilibrium polymer structures and dynamics in bulk and confined soft matter, including manipulation and control of structures, state-of-the-art characterization techniques (e.g., x-ray and neutron scattering, advanced microscopy), and development in theories and simulations. 

Nonequilibrium Structures and Dynamics of Polymeric Materials

Controlling and enhancing polymer material properties by adding nanoscale fillers is well established, but there are still fundamental questions that need to be addressed to advance the field. The goal of the focus session is to review active efforts in creating, understanding, predicting, and modeling structure-property relationships in hybrid polymer materials containing nanoparticles while covering future directions. Areas of interest include, but not limited to polymer-grafted nanoparticles, sustainable materials, highly loaded composites, dynamics, non-equilibrium aspects, kinetically trapped systems, hierarchical structures, material properties, and manufacturing. We welcome contributions from advanced experiments, new theories, and multi-scale computer simulation and modeling.

Hybrid and Multicomponent Polymer Materials Containing Nanoparticles

This session will focus on the physics involved in designing and optimizing recycling and upcycling strategies for polymeric materials. Topics of interest include but are not limited to fundamental studies of polymer properties and behaviors during recycling and upcycling, characterization of recycled and upcycled polymers, and designing novel processing strategies for addressing plastic wastes. Experimental, computational, and theoretical works are welcome. 

Physics in Polymer Recycling and Upcycling

Experiments and simulations of molecular-scale phenomena have revealed unexpected and heterogeneous behavior in polymeric, biological, and soft materials. Recently, single-molecule investigations have shifted toward more challenging physicochemical environments, including polymerization reactions, self-assembled nanostructures, and porous soft materials. This Focus Session invite contributions to all aspects of single-molecule characterization pertaining to polymers and soft matter. Possible topics of interest include but are not limited to: visualization of single polymer chains via fluorescence microscopy, atomic force microscopy, transmission electron microscopy, etc.; measurement of single polymer chain elasticity or secondary structures via optical or magnetic tweezers; quantification of polymerization and diffusion via single-molecule tracer studies; simulation of single polymers during self-assembly, under fluid flow, and in confinement; and the development of new approaches to investigate molecular-scale phenomena in soft materials.

Single-Molecule Characterization in Polymers and Soft Matter

The macroscopic properties of materials are largely determined by their microstructure, which often cannot be changed once made. This limitation applies to most ‘hard’ materials. By contrast, biological and synthetic soft materials such as proteins, tissues, hydrogels, and liquid crystalline elastomers respond to external stimuli (pH, heat, electric/magnetic field, light, or mechanical shear) by changing the structure of constituent components and thus properties, shapes, and motions, which yield adaptive function often inaccessible by manmade materials. Underpinning this contrast is the basic component of biological materials – biomacromolecules with sequentially and/or randomly arranged monomers of prescribed physical and chemical properties. In parallel, incorporating functional components into synthetic polymers and soft materials demonstrates rich physics and unusual properties. This focus session covers recent advances in sequence-controlled polymers, stimuli-responsive hydrogels, responsive soft matter, and their hybrids in the context of chemistry, manufacturing, characterization, multi-function, and applications at various size scales. 

Programmed Responsive Polymers and Soft Matter

While cross-linked elastomers are widely used as both flexible soft materials and highly deformable matrices for polymer composites, the relative role of trapped and chemical crosslinks on their relaxation and mechanical properties remains an open question.  The statistical nature of crosslinked networks makes it difficult to predict the behavior of the network from the properties of the components. Recent development of polymer networks bearing dynamic covalent crosslinks enables materials that are both mechanically robust and processable adds additional layers of complexity in understanding the microscopic relaxations. This symposium will explore recent advances in theoretical, simulation, and experimental studies on the mechanical and rheological properties of permanent and dynamic covalent polymer networks and gels.

Structural Relaxations in Permanent and Dynamic Covalent Polymer Networks and Gels

The physical chemistry of interfacial region between two phases is important in adhesion, lubrication, material design and synthesis, membranes, and colloidal systems.  The interfaces can also be used as the loci for crystallization, polymerization, copolymerization, or grafting. The session is a good fit but not limited to following topics: kinetics of adsorption at the interface, surface/interfacial rheology of amphiphilic macromolecules, self-assembly of block copolymers in solvents and at interfaces, conformation of polymers and block copolymers at the interface, the effect of polymers on colloidal stability, (co)polymerization at the surface, grafting to/from surfaces, the importance of surface forces for pattern formation on surfaces, etc.

Polymers and Block Copolymers at Interfaces

This session focuses on recent advances in the fundamental mechanistic understanding of separation and transport phenomena in polymer membrane materials (e.g., water treatment, carbon capture, resource recovery, ion conductance). The session consists of experimental and computational approaches establishing structure-property relationships through advanced characterization of stimuli-responsive structural changes, morphologies, solute-membrane interactions, and molecular transport processes. Topics of interest include but are not limited to diffusion, sorption, and permeation of gases, liquids, ions, vapors, and other small molecules in membranes fabricated from homopolymers, copolymers, porous coordination polymers (such as metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and hydrogen-bonded-organic frameworks (HOFs)), liquid crystals, and composites of these materials.

Transport Phenomena in Membranes for Separations

Polymer composites play an important role in developing efficient energy storage and conversion systems for the sustainable development of society. This session will focus on the interplay of polymer physics with energy storage and conversion applications. The topics will include but are not limited to, polymer-based electrochemical energy storage devices including batteries and supercapacitors, polymeric dielectric capacitors, fuel cells, polymers for photovoltaic and solar energy conversion, thermoelectric energy generation, etc. The polymer physics components will be focused on the role of polymer architecture, morphology, mobility, ion transport, phase separation, crystallization, confinement effects, type of nanocomposite filler, the shape and dispersion of filler, and confinement effects, etc. on the energy storage and conversion performance. 

Polymers and Polymer Composites for Energy Storage and Conversion Applications

The behavior of polymers with  topologies beyond linear, including inherent structures such as rings and combs as well as induced topologies by  permanent and dynamic constraint,  will be discussed.  This session will introduce new physics of polymers  “beyond-linear”, and including but not limited to the conformations, dynamics, rheology, self-assembly, and mechanical properties of topological  polymers. The overarching goal is to provide a platform to present  the state-of-the-art research on topology- polymers’ physics, their relation/analogy to biomolecules, and spur new ideas to expand the applicability of these polymers.  

Beyond Linear: Structure, Dynamics, Response and Unique Properties of Topological Polymers

Ultra-dense glasses can be obtained by extreme aging, by vapor deposition methods and by high pressure glass formation processes.  The different paths to ultra-stability lead to novel insights into the behavior of amorphous materials in the deep glassy state.  Papers are invited in which fundamental questions of the glass transition and deep glassy state dynamics are addressed through experiment, theory and simulation of the ultra-dense glass.  Of particular interest are works in which evidence of novel heterogeneous dynamics as well as extreme relaxation times or viscosities are observed. 

Ultra-Stable Glasses

This session seeks abstracts that describe the underpinning physics from the perspective of soft materials processing associated with advanced manufacturing technologies. These technologies can include 3D/4D printing, biomanufacturing of soft materials, novel composite processing, and others. This session will be interdisciplinary, covering material physics, multiscale simulations, manufacturing engineering, polymer synthesis, and performance characterization. Topics of interest include new printing techniques, materials, analytical tools, and insights into the physics of 3D printing of polymers, colloids, and gels.

Physics of Soft Materials Advanced Manufacturing

Since the first use of the “Scotch tape method” to isolate monolayer graphene, interfacial phenomena between polymers and two-dimensional (2D) van der Waals materials have been at the frontier of much new and intriguing solid-state physics. The aim of this Focus Session is to highlight recent research into adhesion, wetting, friction, surface passivation, charge interactions, and other physical phenomena occurring at the interface between 2D materials and polymers.  Also of interest is design and engineering of these interactions to fabricate novel photonic and electrical heterostructure devices, improve flexible electronics, synthesize membranes with enhanced stability or transport properties, and generate nanoscale self-assembled patterns.  Experimental, theoretical, and computational contributions are all welcome.  

Physical Phenomena at Polymer/2D Material Interfaces

This session aims to bring together researchers from various electrostatic communities from DSOFT, DPOLY, DBIO, and DFD including, electrospinning, electrospray, ionic propulsion, electrophoresis of active matter, electroporation, electrowetting, electrohydrodynamics, liquid crystal and block copolymer orientation, and soft robotics. These fields share many of the same challenges, but often do not communicate, which leads to there being perceived gaps in the fundamental knowledge base that is hiding in an adjacent field. The purpose of this session is to build dialog and identify opportunities and challenges for future investigation of the field as a whole. 

Electrostatic Manipulation of Fluids and Soft Matter

Advances in molecular biology have put a focus on phase transitions as a fundamental mechanism underlying cellular organization, with various proposed roles in regulating the complex reaction network that underlies cell function. Yet, apart from their biological import, phase transitions exhibit rich physics, e.g., unique dynamics (nucleation and growth) and spatial patterning (spinodal decomposition). Model phase-separating systems provide a powerful means to explore this physical landscape. Such systems bypass the complex environment inside the cell, permitting control and quantification of the nanoscale particle structure's effect on condensate properties, the mesoscale behaviors of droplets induced by their environment (e.g., droplet/matrix or droplet/droplet interactions), and the macroscopic organization and mechanics of droplet assemblies. This focus session would present recent exciting discoveries made using such bio-inspired systems, including not just findings relevant to biological condensates, but also new physical phenomena. 

Bio-inspired Phase-Separating Systems

DNA is broadly employed as a building block to design complex systems - from soft materials to neural networks. DNA naturally exists in a wide range of sizes and different topologies and can thus be used as a proxy to answer fundamental questions on entanglement and viscoelasticity. Further, naturally occurring, sophisticated proteins can be used to precisely alter DNA topology and mechanics - turning DNA into an active matter platform with a range of ground-breaking applications - from healthcare to energy and information storage. At the same time, the precise and affordable synthesis of long DNA sequences is fostering the exploding field of DNA nanotechnology. This session will focus on the physics of DNA-based materials, from complex fluids and gels to soft structures. Through a combination of experimental and theoretical work, we will elucidate the physical principles that determine the unique properties of diverse systems that can be made with this fascinating macromolecule.

DNA-based Soft Matter: Design, Dynamics, and Active Mechanics 

Microscale mechanical and rheological characterization enables unprecedented insight into the structure-mechanics relationships of a wide variety of soft material systems from polymer networks to granular materials to living cells. While traditional microscale methods focus on determining linear viscoelastic moduli, recent advances have greatly expanded the properties, spatiotemporal scales and materials that can be investigated and measured. This session will examine emerging trends in combining micromechanical analysis, microrheology, imaging and spectroscopy, including high-throughput methods and methods to access nonlinear properties, to study both classic soft materials as well as out-of-equilibrium active matter and heterogeneous noisy systems. 

Emerging Trends in Soft Microscale Mechanics

Recent advances in synthesis and computational design approaches have opened up new avenues for understanding the mechanisms that drive the organization of structural order in soft materials. Following these new approaches, interesting pathways have emerged to create novel structures with various degrees of complexity, including those that have not been previously observed in atomic or molecular systems. This focus session will feature recent work in complementary settings from theoretical and experimental groups. We believe that this focus session will help catalyze discussions for realizing new forms of structural order in soft matter. 

Engineered Soft Materials: New Approaches, Mechanisms and Structures 

Friction, fracture, and adhesion are nonlinear and often dynamic, phenomena occurring on many scales with important implications to many engineering, biological, and geophysical applications. This session solicits contributions computational modeling and experimental investigations relevant to these phenomena with particular emphasis on soft materials including polymers, hydrogels, and granular matter. Possible topics may include but are not limited to: Role of topology and bulk patterning on interfacial adhesion and fracture including potential non-reciprocity, experimental observations of crack nucleation and propagation at different length scales especially those with mechanophores, particularly, and computational modeling of fracture and adhesion in soft materials. In many situations, macroscopic properties of interfaces crucially depend on the physical processes localized in a process zone where meso-structures emerge spontaneously or are triggered by heterogeneities. The size of these meso-structures usually far exceeds the molecular scale, while remaining smaller than the system size allowing to adopt continuum approaches. Complex dissipation mechanisms occurring at crack tips and adhesive interfaces are mediated by instabilities including fibrillation, cavitation and crack branching leading to unsteady dynamics whose understanding remains an open and challenging question for experimentalists and theoreticians.

Friction, Fracture, and Adhesion in Soft Materials

Gels, nonfluid networks of particles or polymers that are pervaded by fluid, appear ubiquitously within soft matter in practical applications as well as in living biological systems. The mechanical properties of gels are intermediate between those of fluids and solids, and depend sensitively on the structure of the gel constituents across multiple length scales. This focus session invites experimental, theoretical, and computational studies of the rheological properties of gels, including chemical and physical gels, hydrogels, colloidal gels, and biological gels, with particular interest and emphasis on connecting structural properties to flow properties. Contributions examining the effect of non-equilibrium activity (driven by molecular motors or by active particles) on gel mechanics are encouraged.

Gel Rheology

In the large family of active matter, active polymers are a class of active systems of considerable interest due to their prevalence in biological systems on multiple length scales. On the nano and microscale, biology provides many examples of active, flexible structures ranging from actin filaments and microtubules to flagella in sperm, algae, bacteria, and a diversity of other planktonic microorganisms. Understanding the non-equilibrium statistical mechanics of such active systems is a major challenge, both experimentally and theoretically. This focus session aims at bringing together people studying the physics of active polymers in various situations.

Active Polymers: Organization and Dynamics

This invited session would include contributions from theoretical and experimental researchers working on polymer phase separation and self-assembly topics relevant for biology (e.g. coacervation, liquid-liquid phase separation of intrinsically disordered proteins, and similar) but also more general materials physics (liquid crystalline polymers, macrocycle-based coordination hydrogels, polymer-surface adhesion). 

Mechanisms of Self-Assembly: Biology and Beyond

Ions at charged interfaces are fundamental to a wide range of phenomena, from biology to industry. Although a relatively better understanding of point-like monovalent ions exists, our knowledge on multivalent and complex ions at aqueous interfaces are still very limited. This session aims to bring experimental and theoretical experts together to discuss the recent developments in the field including but not limited to ion-ion correlations, ion-amphiphile interactions, and ion transport at charged interfaces with a focus on multivalent ions.

Multivalent and Complex Ions at Charged Interfaces

Textiles are some of the most ubiquitous materials in our every day lives. They are incredibly strong, lightweight and have low bending rigidity for their weight. However, very little is known rigorously about the physics that underlies these behaviors. The complexity of this problem arises from the numerous knots and tangles that convert 1D threads into 2D surfaces with 3D geometry. Although knits and weaves are primarily engineered, nature has also generated many 2D structures from filamentous material with a myriad of self-assembly techniques. This session aims to target the multiscale physics that gives stretchiness to our sweaters and pleats to our pants.

Textiles and Topology: Physics of Knots and Tangles

This focus session to bring together researchers coupling elastic continua with other classical mechanical systems such as fluids, fluid surfaces, fibers, and grains. The goal is for researchers to see the nuance and complexities which arise when coupling elastic systems in different ways, enabling learning and collabouration across different materials and disciplines.

Elasto-X, Combining Elasticity with Capillarity, Granularity, Adhesion, and Others 

From fundamental nonequilibrium statistical physics to polymer physics and deformation and flow of disordered solids,  this Focus Session will bring together scientists from a broad range of  physics whose fundamental statistical and non-linear physics studies have advanced  materials applications.

From Statistical Physics to High Performance Materials.

The structure and behavior of animal cells including locomotion or decision making strongly depend on the physicochemical properties of the extracellular matrix (ECM) in tissues and cell cultures. To that end, how a porous, viscoelastic medium, such as the ECM or its synthetic analogs, affects the reconfiguration of an isolated single cell in vitro or in vivo requires a fundamental biophysical understanding. This will elucidate the microscopic origins of tissue mechanics and growth, as well as stem cell differentiation or malignant cell migration. Further, it may provide insights into tissue self-organization through matrix-mediated mechanical interactions between isolated cells and motivate new physical models of active matter in elastic media. The goal of this focus session will be to provide a comprehensive single cell-level mechanistic perspective for the dynamical coupling between the cell and the medium in two and three dimensional settings. To achieve that, the session aims to bring together presentations of experimental, theoretical, and computational research from a broad range of speakers with different backgrounds.

Mechanobiology of Cell-Matrix Interactions

Flow of Complex Fluids: Rheology, Structure and Instabilities

Focus Sessions

Semi-Crystalline Polymers

Liquid Crystalline Polymers

Polymer Glasses and Glass Formation

Polymer Rheology

Polymeric Networks, Elastomers, and Gels

Charged and Ion-Containing Polymers

Polymer Composites

Electrically and Optically Active Polymers

Surfaces, Interfaces, Thin Films, and Coatings

Biopolymers and Sustainable Polymers

Polymer Structure, Morphology, and Self-Assembly

Standard Sorting Categories

Polymer Physics

DSOFT Symposium Invited Speaker (Invitation Only)

Recent advances have been made in the fundamental understanding of transport phenomena and dynamics of soft materials, specifically carbon and hydrogen adsorption, separation and storage, and energy-efficient water purification. These include the morphological characterization and host-guest interactions within materials under various environmental conditions. Both experimental scattering/spectroscopy/imaging and computational studies are encouraged.

Emerging Soft Materials for Gas Separation and Water Purification

Tribocharging is a ubiquitous phenomenon present at a mesoscale. However, there is no consensus within the scientific community concerning the exact mechanism of its occurrence. The role of water in the tribocharged system is becoming increasingly popular among scientists. This phenomenon is not only limited to the water coverage on hydrophilic and hydrophobic surfaces but also crucial concerning the tribocharging of chemically similar materials. Recently, it has been reported that water movement past substrates negatively charges the respective substrates. Altogether, these new insights are advantageous to the emerging area of energy harvesters, such as triboelectric nanogenerators (TENGs) or colloidal crystals. 

Tribocharging and the Role of Water

From soft robotics to 4D printing research labs abound with examples of smart morphable matter capable of interacting with their environment solely on the basis of their material properties. These responsive, malleable and programmable materials often derive their remarkable functional properties from their structure rather than their chemistry alone. We are seeking contributions studying the fundamental and practical aspects of such morphing materials. Particularly, we are interested in (1) the mechanisms of amplification of an input via the architecture of the materials and (2) the programmability of a complex response using a simple mode of actuation.

Morphing Matter: From Soft Robotics to 4D Printing

The rheological behavior of multiphase systems is controlled by composition, morphology, time scale, flow field, relaxation time(s), thermodynamic interactions between phases, the presence of surfactants, etc. In addition, the structure of multiphase systems can be influenced by flow-induced phenomena. Therefore, the structure-rheology relationship of multiphase systems is of interest.

Rheology of Multiphase Systems

Bio-inspired Phase-separating Systems

DNA-based Soft Matter: Design, Dynamics, and Active Mechanics

Active matter is an exciting current topic of discussion the spans across length scales. At the nanoscale, recent exciting results on enzymes and their ability to enhance their diffusion rates due to catalytic turn-over have advanced the ideas that the principles of self-organization in active systems are relevant even for individual proteins. In addition, these proteins have been used as propellants for larger, microscale active matter systems, so understanding their mechanism of action at the single enzyme level will inform their collective behavior. This session focuses on enzyme driven active matter from single molecules to collective motion of micron-scale systems.

Enzyme Driven Active Matter from Nano to Microscale

Friction, Fracture, and Adhesion in Soft Materials 

Climate change poses an immense number of scientific and technological challenges involving the physics of soft materials. Examples span an enormous range of length and time scales: the flow and fracture of glacial ice, granular debris flows after forest fires, oil spill remediation, and sustainable cement. This session aims to highlight the broad range problems in this interdisciplinary field and importantly, provide a platform for researchers to communicate and share ideas. In 2022, attendance for this session was very good, so we hope to continue this trend and attract more submissions outside of normal DSOFT sorting categories.

From Grains to Complex Flows to Sustainable Materials: Soft Matter Meets Climate Change

Learning is most frequently associated with natural and artificial neural networks. But learning is a broader metaphor that can apply to non-neural physical and biological systems. Many non-neural systems can accumulate incremental changes due to their response to a sequence of environments and thus learn desired behaviors. This session will bring together speakers working on diverse examples of physical systems that exploit past history to respond correctly to future environments. By exploring this theme in diverse areas, this session aims to crystalize common themes like learnability and constraints due to biologically and physically realizable local learning rules. 

Learning in Physical Systems With and Without Neurons

As computer technology reaches physical limitations, and manufacturing and design of complex structures is rapidly accelerating, alternative platforms for low-energy computing or computing in harsh environments are emerging. In particular, mechanical approaches are attractive due to their natural incorporation of strong nonlinearities, central for material based logic, neural networks, and learning. Memory is finding new applications as an organizing principle for history dependence, programmability, adaptation, and design in non-equilibrium matter. Recent examples include mechanical memories in granular packings, colloidal gels, metamaterials, and origami structures, as well as memories of thermal history, magnetic fields, or even evolutionary pressures. Examining memory can yield unexpected parallels between seemingly disparate systems. This session is an opportunity to observe common principles, patterns, and distinctions from many examples in current soft matter research.

Infomatter: Discovery and Design of Memory Formation and Information in Matter

Highly deformable soft structures find applications when safety, adaptability, and morphing are leading requirements. However, their true potential is unveiled when these structures are designed to harness nonlinearities, such as snap-through/snap-back instabilities, buckling, and bifurcations. This results in unprecedented functionalities such as embedded decision making, multistability, passive sensing, mechanical computing, and energy trapping. The purpose of this session is to highlight recent advances and future directions in the field of soft structures driven by structural nonlinearities. Examples of the topics include (but are not limited to): modeling and simulation of instabilities in soft systems; design, fabrication, and testing of instability-driven soft systems; generative design of soft systems with programmable instabilities; demonstration of novel functionality through structural nonlinearities; and embodied functionalities (energy, sensing, actuation, logic) in soft robots.

Functionality through Structural Nonlinearities in Soft Systems

Soft and active matter systems display rich emergent phenomena and phases, often in combination with disordered structure and slow relaxation times. The complexity of the constituents means that the effective interactions underlying this emergent behavior can be non-reciprocal. Mechanically, this leads to apparent violations of Newton’s 3rd law. Alternatively, social interactions can lead to non-reciprocity in populations of organisms. This session will bring together researchers working on distinct areas of non-reciprocity, including organism-scale constituents, interactions between microparticles mediated by a non-equilibrium environment, and a variety of other far-of-equilibrium systems. We hope to highlight a broad range of experimental and theoretical phenomena recently discovered across a variety of soft matter domains, and encourage collaborations and the sharing of ideas between diverse participants.

Non-reciprocity in Soft and Active Matter

This focus session is a follow-up of the “Active and externally driven granular matter” Focus session started in APS March meeting 2022. It aims to be a meeting point between the physics communities working with granular and non-colloidal active matter. Despite the difference the mechanism for energy input -at the individual particle level versus globally-, both “active granular” and “driven granular” are athermal materials that share common features as large density fluctuations, emergence of behavior, descriptions based on effective temperature, and long-range correlations. The goal of this focus session is to bring together experts coming from both ends to share their knowledge and expertise to try and identify key features and ingredients to understand the physics behind these systems.

Externally Driven and Active Granular Matter

The experimental and computational study of soft matter often generates significant amounts of data (from, e.g., scattering experiments, rheological characterization techniques, particle-based simulations, amongst many others). A challenge common to all these endeavors is rigorously quantifying uncertainties associated both with the data itself and with models informed by this data (at a level substantially beyond “vanilla error bars”). As applications of data-driven methods to describe soft matter proliferate (and especially given that many modern data-driven techniques are capable of systematically accommodating uncertainties), it is more critical than ever before to nucleate conversations about uncertainty quantification amongst soft-matter physicists. 

Uncertainty Quantification for Soft Matter Physics

Fluid mechanics is a key element in many biological systems at all scales. From cells and tissue, to organ and organism, it is often central to the functional transport of fluids and chemicals, as well as the growth or actuation of flexible membranes. These functionalities are driven by the nonlinear coupling between fluids and structured soft interfaces that can be actuated by other mechanisms and show time dependent mechanical properties. Amongst many examples, the regulation of cell volume, migration and development in bacteria, plants and animals is often programmed by the interplay between static fluid pressure and the viscoelasticity of membranes or cell walls. For vascular networks in animals, plants, fungi and slime molds, deformations based on locally distributed feedback provide fluid transport (e.g. lymphatic system) and adaptation of network morphology through growth of new channels or narrowing of existing ones. At the interface between fluids and soft membranes, active mucus-cilia hydrodynamics control the flow of fluids with complex rheological properties.

In recent years, physicists have aimed at building a fundamental understanding of these biomechanical systems and drawing inspiration from them to design new materials and structures and develop new concepts in mechanics. The goal of this Focus Session is to bring together scientists from diverse and complementary communities, across biomechanics, physics, applied mathematics, and engineering, from a variety of analytical, computational and experimental approaches to advance our understanding of biological fluid mechanics.

Biological Fluid Mechanics of Transport, Growth and Actuation

Elasto-X, Combining Elasticity with Capillarity, Granularity, Adhesion, and Others

Thin materials abound both in nature and in scientific applications, and may be observed from microscopic to macroscopic length scales. Though they arise in different contexts, thin materials may exhibit similarities in their rich behavior and instabilities, from wrinkling, to buckling, to crumpling. In this session, we will explore the deformations of thin sheets and shells, including the dynamics of buckling transitions and hysteretic events, the role of geometry and frustration in their stability, and the morphologies and mechanical properties of buckled materials.

Buckling Instabilities of Thin Materials

Many real systems of interest in physics, engineering, social and computer sciences, biology and finance can be represented as networks: the Internet, the World Wide Web, social networks, transportation systems, scientific citation graphs, biochemical and ecological networks, and financial credit graphs are just a few examples of real systems that can be modelled as networks, and thinking of them in this way can lead to useful insights. Network science has produced many fundamental results for the description of critical phenomena in real-world networks. These represent essential tools for the control and design of real systems with concrete and practical applications. For example, thanks to the results of network science, we know how to design a computer network that is less vulnerable under intentional or random attacks, what is the best strategy for the immunization of a social network subjected to the spreading of a virus, how to identify the most influential nodes in a network, and how to drive a dynamical system living on a complex network towards the desired state. The number of real-world applications of network science is literally exploding due to the growing availability of data and computational resources. The goal of this Focus Topic is to address recent advances in the network theory of real-world complex systems.

Network Theory and Applications to Complex Systems

In recent years, machine learning techniques have been applied to nonlinear and complex dynamical systems to solve previously unsolved problems in the field. For example, a research area based on reservoir computing, a class of recurrent neural networks, has gained considerable momentum and emerged as a powerful paradigm for model-free prediction of the state evolution of nonlinear and chaotic dynamical systems. Machine learning methods have also been developed to predict critical transitions, transient chaos, and Hamiltonian chaos. This Focus Session will bring together a number of active researchers to discuss current issues in this emergent interdisciplinary field.

Predicting Nonlinear and Complex Systems with Machine Learning

In this Focus Session, we plan to bring experts from the statistical physics and machine learning communities together to present the newest results and possible directions for understanding and advancing AI research based on ideas and tools from physics in particular statistical physics.

Statistical Physics Meets Machine Learning

Recently, there has been impressive progress to quantitatively
characterize noise-driven dynamics in far-from-equilibrium systems. Examples abound in diverse systems including active biological matter, climate models, nano-mechanical oscillators, and electronic networks. Common features include the measurement of non-vanishing steady-state probability currents, the development of novel metrics to quantify distance from equilibrium, the characterization of detailed balance violation, and the generalization of fluctuation-dissipation relations. This Focus Session is targeted to experimentalists and theorists working in biophysics, nonlinear and statistical physics, and condensed matter physics, for whom it will be stimulating to explore emerging experimental techniques and analytical tools for understanding the noisy dynamics of far-from-equilibrium systems.

Noise-Driven Dynamics in Far-From-Equilibrium Systems

The transport of fluid-suspended objects by various driving forces is ubiquitous in natural systems. Examples include the transport of small particles in the ocean, aerosols in the atmosphere, dust particles in plasmas, and cellular components in biological systems. Until recently, studies of such “steerable particles” lacked recognition of their common theme. The goal of the Focus Session is to encourage a broad range of scientists who work on these driven systems to get together and identify common interests, possible collaborations, and future directions. In particular, we hope to strengthen the interface between the communities of colloids and of nonequilibrium physics.

Steerable Particles: New Ways to Manipulate Fluid-Mediated Forces

The physics of neural dynamics have fundamentally changed our understanding of how matter stores and processes information. Such understanding enables us to take advantage of neural information processing through models of neural activity such as convolution neural networks that learn complex relations in visual data. Beyond image recognition, biological brains incorporate a history of information and perform cognitive tasks to make decisions, change their belief about the world, and abstract knowledge. How does a dynamical model of neural activity achieve these cognitive functions? The goal of this Focus Session is to explore the mechanisms of how neurodynamical models achieve cognitive function.

Neurodynamical Models of Cognition

The binding of proteins to cellular membranes and the organization of these membrane-bound proteins are key to many cellular functions. These binding and assembly processes occur over a wide range of lengthscales, from sub-nanometer to tens of microns, and timescales, from nanoseconds to tens of minutes. As such, a variety of experimental and computational techniques have been used to study the mechanochemistry of lipid-protein interactions. Yet, resolving and connecting the structural and dynamical aspects across scales remain challenging in both experiments and simulations. Current methods in both experimental and theoretical treatments of this problem are reaching the state where communication between researchers and across scales is possible, and necessary for the science going forward. The aim of this Focus Session is to bring together researchers working on experimental and computational aspects of these different scales in hope of getting closer to a multiscale understanding of protein binding and assembly on cell membranes.

Binding and Assembly of Proteins on Membranes

Maimbourg et al. recently obtained a classical dynamical mean-field theory (cDMFT) of equilibrium glass formers. Although the existence of such a description had long been postulated, its first-principle derivation provides physical insights into contexts far beyond the original system of interest. It has notably been generalized to out-of-equilibrium situations, such as externally driven and active systems, as well as to finite-dimensional models with tree-like interactions, such as ecological communities and machine learning algorithms. By bringing together physicists that have developed these applications of cDMFT, this Focus Session will demonstrate the remarkable versatility of the approach and stimulate further developments.

Dynamical Mean-field Theory (DMFT) for classical systems of interacting objects

Information theory concerns the relationship between data, models, and reality. Its striking generality allows information theory to show up in myriad places and lend a powerfully unifying perspective. From simple biological entities processing observations to deep neural networks cutting through mountains of data, how are models formed?  Is there room for improvement, or are there limiting constraints? Deconstructing the great model that is physics, how are notions of equilibrium and order, and even the arrow time, cast in terms of information? Coarse graining and the renormalization group operate in an abstract space of models -- how are these pillars of statistical physics fundamentally connected to information theory? With a rich variety of research and common language with which to spark connections, this Focus Session has potential to educate and inspire physicists at all levels of familiarity with information theory.

Information Theory and Physics

While steady-state fluid dynamics and near-equilibrium mechanics of granular and soft materials are relatively well-understood, much less is known about transient and far-from-equilibrium responses of these materials to external stress and/or strain forcings. Such responses include stress overshoot at startup, shear jamming, transient shear banding and yielding, creep, and structural and mechanical evolution during oscillatory deformation. Furthermore, there is lack of theoretical models that can describe such nonequilibrium responses of these materials with microstructural fidelity. One invited session together accompanying focus session will address these non-equilibrium phenomena of granular and soft materials.

Non-equilibrium and Transient Mechanics of Granular and Soft Materials

From Statistical Physics to High Performance Materials

Understanding the concepts and principles underlying the molecular machines present in the cell will guide the design of versatile variants with direct impact on health, disease, and synthetic biology. This session will cover a wide range of topics including but not restricted to the behavior of the amazing molecular machines of life, fluctuations and dissipations, allosteric-driven motions, chemomechanical action, and the remodeling and translocation of biopolymers. Experimental, computational and theoretical contributions are all welcome. Examples of areas of interest are the study of phenomena that span multiple length and/or time scales and the introduction of novel ideas and techniques in chemistry and physics. Approaches that combine traditional methods with machine learning techniques to uncover novel aspects of biomolecular machines are encouraged.

Molecular Machines

Macromolecular phase separation of proteins, nucleic acids, and sugars plays a fundamental role in a wide range of cellular processes. These processes often depend on the composition and material properties of the resulting biomolecular condensates, which are usually multi-component and sometimes multi-phasic. Open questions include (i) how biophysical properties of the condensed phase and interface, from density and viscoelasticity to surface tension and wetting, emerge from the interactions between constituent macromolecules; (ii) how aging, maturation, and interaction with surfaces modulate these properties and their biological function, and (iii) how condensates influence chemical reaction networks, are regulated within cells and tuned by evolution. Progress towards understanding general principles requires quantitative measurements on diverse experimental systems, from model polyelectrolyte-complex coacervates to the full complexity of in vivo condensates, combined with new theoretical and computational frameworks to both describe sequence-dependent interactions between heteropolymers on the molecular level and to account for non-equilibrium dynamics on the cellular scale. This focus session aims to foster interdisciplinary communication and collaboration in this exciting area by bringing together experimental, computational, and theoretical experts from polymer science, soft matter physics, bioinformatics, and biology.

Biomolecular Phase Separation

Rapidly mounting evidence suggests that the mechanical properties of cells and the extracellular matrix impact a wide variety of biological functions such as cell shape, motility, gene expression, cell fate selection, and tissue functions. The goal of this focus session 'Mechanics of Cells and Tissues’ is to bring together researchers from various disciplines working on cell and tissue mechanics, chemomechanics, and mechanobiology to share their recent experimental and theoretical findings. By doing so, this session will provide a broad understanding of the role that mechanics plays in regulating the structural organization and function of living cells and tissues, across the molecular to the multicellular scales. The session will feature recent advances in experimental and computational methods that help probe these questions. We welcome submissions from researchers working on a broad range of topics under the cell and tissue mechanics umbrella. Examples include, but are not limited to, passive and active behaviors, mechanical phases of individual cells and cellular collectives, mechanical heterogeneity in cells and tissues, viscoelasticity, plasticity and fracture of cells and tissues, tissue shape changes and folding, as well as the interplay of mechanics with genetic and biochemical factors, and the thermodynamics of cell energy usage.

Mechanics of Cells and Tissues

State-of-the-art efforts to understand life at the level of biological cells are robustly successful in the two disparate regimes of structural biology and systems biology. Decades of work have yielded numerous landmark results focusing on these two well-separated length scales. In one limit, structural biology captures atomistic physics over nano-seconds of dynamics, yielding tremendous insight into e.g. protein assembly and folding. In the opposite limit, systems biology predicts whole-cell behavior over minutes of function, but at the expense of abstracting physics away entirely. In the intervening mesoscale physics between these two limits, organizational and dynamical principles are rapidly emerging as a new frontier of exploration of whole-cell function. Collectively, the contributions to this session will span atomistic to whole-cell physics and generate connections between researchers seeking physics-based understanding of whole-cell function, with an overall balance of experiment and modeling. Talks focused on mesoscale physics are particularly sought.

Multi-scale Modelling of Biological Cells: From Atoms to Whole Cells

Biomaterials and Nanotechnology

Collective Behaviors in Biology

Biological Active Matter

Constructed by many interacting components, all biological systems, ranging from molecular assemblies to tissues and societal groups, exhibit emergent phenomena such as pattern formation, large-scale flows, and dynamical instabilities, essential to their function. We encourage submissions that uncover the physics of collective behaviors in specific biological systems as well as studies aiming at unifying descriptions across scales.

Disordered, Glassy, and Jammed Behavior in Biological Matter

This session bridges together physicists using theoretical and experimental approaches to understand complex ecological dynamics across the full spectrum of spatio-temporal scales ranging from small communities to entire ecosystems. Topics of interest include the ecological rules that govern the assembly, turnover, function, dynamics and stability of these systems and the effects of time-varying environments.

Non-Equilibrium Thermodynamics: From Natural Selection to Chemical Reaction Networks

Pattern Formation in Biological Systems

Physics of Neural Systems

Social interactions shape our lives. Yet, their complexity challenges our ability to understand, model, and mimic optimal social network structures. What rules govern the complex dynamics of social interactions in living systems? Topics in this realm vary from systems of flocking birds and schooling fish performing collective behavior, to the intricate interplay between small groups of individuals during activities such as courtship and aggression. Our session builds on this momentum and will bring together physicists thinking about the social interactions between biological systems. The primary goals of our session are to identify key questions and organizing principles in the nascent field of the Physics of Social Interactions and to encourage new collaborations and directions over the coming years. In addition, we will explore the boundary between such animate and related inanimate physical interactions, how social interactions are governed by physics, and vice versa, and how other interactions between entities we tend to consider as inanimate could be social. We will draw from a diverse range of efforts to address it, including experimental work as well as theoretical, computational, and robotic models.

Physics of Learning: Learning and adaptation in living systems

Neural systems are a fascinating source of challenging problems for physicists, attracting a wide range of approaches to understand the emergence of cognition and intelligent behavior. From an empirical perspective, modern recording techniques continuously motivate the development of new methods for analyzing, modeling, and interpreting highly complex data, collected in increasingly naturalistic settings. In parallel, physics approaches stimulate the design of new experiments by generating normative theories of brain computation. Finally, a complementary approach to understanding intelligent systems is provided by artificial neural networks and machine learning. These systems have reached superhuman performance on a range of well-constrained tasks, but still lack the robustness and versatility of animal learning. This Focus Session welcomes this diversity of approaches, with the goal of enhancing communication and synergy within a highly interdisciplinary community. We hope the session will spur discussion and exchange of ideas on how neural systems represent and process information to enable robust learning.

Robophysics

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; and peta-to-exascale algorithms for hybrid deep learning and MD simulations.

Emerging Trends in Molecular Dynamics Simulations and Machine Learning

Multi-scale Computational and Theoretical Methods in Molecular Biophysics

Drops

Active Colloids

Thin Films, Surface Flows and Interfaces 

Swimming, Motility, and Locomotion

Transport Phenomena in Heterogeneous and Dynamic Environments: From Colloids to Active Matter

Colloids and Granular Materials

Emulsions and Foams

Liquid Crystals

Membranes, Micelles and Vesicles

Gels and Complex Fluids

Disordered and Glassy Systems (non-polymeric)

Fracture, Friction, and Deformation

Self-and Directed Assembly (Equilibrium and Non-equilibrium)

Active Materials

Rheology and Flow of Soft Materials

Mechanical Metamaterials

Extreme Mechanics

Soft Condensed Matter

GSNP Symposium Invited Speaker (Invitation Only)

The recent revolution in non-equilibrium statistical physics has allowed researchers to significantly advance and generalize Landauer's original results concerning the thermodynamics of bit-erasure into a full-fledged "thermodynamics of information processing," which analyzes computational systems ranging from information ratchets to digital circuits, all the way up to Turing machines. At the same time, our understanding of the information processing within biological cells has greatly expanded, driving an explosion of work on the thermodynamics of biological information processing, including processes such as polymerization, kinetic proofreading, and cellular sensing. Recently researchers have started to consolidate these research thrusts into a unifying thermodynamics of information processing/computation in both biological and artificial (human-engineered) systems. In addition, the community is now beginning to apply stochastic thermodynamics more broadly, to everything from social opinion networks to replicator dynamics to electronic circuits to neurobiology. The aim of this Focus Session is to continue to deepen our understanding of this important set of issues.

Stochastic Thermodynamics of Biological and Artificial Information Processing

Ever since its inception in the 1950s the notion of a "quantum heat engine" has been at the forefront of research in quantum thermodynamics; however, only over the last five years or so have experimentalists started to succeed in realizing genuinely "quantum" engines. In particular, the global push for the development of quantum technologies has sparked concentrated efforts to produce new devices. At the same time, with the rise of quantum technologies, understanding the laws governing energy, entropy and information flows between quantum systems is of crucial importance. Practical motivations are to keep a fair account of the resources to process information in the quantum realm, and optimize them with respect to well-defined performances. The question of the physical resources potentially consumed by quantum computing (whether deterministic or reservoir-assisted) has been largely overlooked so far, while it may become a bottleneck for scalability. This session aims to open an interdisciplinary dialog and lay the ground of new research addressing these questions.

Thermodynamics in Quantum Information

The brain is a physical system of daunting complexity, characterized by heterogeneous patterns of structural connections supporting unparalleled feats of cognition and a wide range of behaviors. Understanding how the brain’s structural wiring develops, and how this wiring supports collective neural activity and cognitive functions, remain fundamental challenges for physicists and neuroscientists alike. In this Focus Session, we will explore how theories and intuitions from statistical mechanics can be used to uncover the emergent structure and function of the brain.

Statistical Mechanics of the Brain

Biology studies traits of organisms and the mechanisms that generate those traits. With the rise of quantitative biology, the variability in measurable traits has often been seen as a challenge. However, biological variation is the basis of evolution, and the mechanisms generating that variation are important for organismal fitness and adaptation. Thus, this Focus Session will explore how the structure of noise in biological systems can reveal, rather than obscure, characteristics of biological systems. The speakers will discuss variability as a biological trait and, by attributing biological noise to physical mechanisms, consider the limits of precision that biological experiments can attain.

Variability in Biological and Living Systems

The session covers both theoretical aspects as renormalization group of graphs, as well as, applications on life sciences (Food, Brain, Epidemics) and Society. We believe that this mix is very interesting both for practitioners and newcomers into the field.

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