Research
I am interested in the general area of Non-Equilibrium Statistical Physics and its application in driven Soft and Biological matter, using various analytical and numerical tools, such as molecular dynamics (Brownian, Newtonian or Langevin) simulations, hydrodynamic and stochastic (Langevin and Fokker-Planck equations) descriptions, and various data analysis techniques etc.
Glassy dynamics in dense active matter: We have studied active Kob-Andersen glass to see how activity induces cage breaking and fluidization and obtained the phase diagram in the (temperature -activity) plane, showing that the glassy phase disappears beyond a critical activity. We demonstrate that activity induces a crossover from a fragile to a strong glass and a tendency for clustering of the active particles.
Glassy dynamics in dense active matter: We have studied active Kob-Andersen glass to see how activity induces cage breaking and fluidization and obtained the phase diagram in the (temperature -activity) plane, showing that the glassy phase disappears beyond a critical activity. We demonstrate that activity induces a crossover from a fragile to a strong glass and a tendency for clustering of the active particles.
See Soft Matter, 12, 6268 (2016) for details.
Glassy swirls in dense active dumbbells: We find dramatic differences both in the fragility and in the nature of dynamical heterogeneity in a dense assembly of active dumbbells. The active supercooled liquid exhibits large swirls and vortices, whose scale, set by activity, appears to diverge as one approaches the glass transition. We have formulated a hydrodynamic description that provides an explanation of these observations.
Glassy swirls in dense active dumbbells: We find dramatic differences both in the fragility and in the nature of dynamical heterogeneity in a dense assembly of active dumbbells. The active supercooled liquid exhibits large swirls and vortices, whose scale, set by activity, appears to diverge as one approaches the glass transition. We have formulated a hydrodynamic description that provides an explanation of these observations.
See Phys. Rev. E, 96, 042605 (2017) for details.
RFOT theory for active glass: We extend the Random First-Order Transition theory to a dense assembly of self-propelled particles. We compute the active contribution to the configurational entropy using an effective medium approach which provides excellent quantitative fits to existing simulation results and makes several testable predictions, which we verify by both existing and new simulation data.
RFOT theory for active glass: We extend the Random First-Order Transition theory to a dense assembly of self-propelled particles. We compute the active contribution to the configurational entropy using an effective medium approach which provides excellent quantitative fits to existing simulation results and makes several testable predictions, which we verify by both existing and new simulation data.
See Proc. Natl. Acad. Sci. USA, 115 (30), (2018), for details.
Active transport and diffusion in cell membrane: We have studied the dynamics of particles advected in an active quasi-two-dimensional medium consisting of self-propelled filaments. We find that the particles show a tendency to cluster and non trivial density fluctuations. The late-time dynamics is diffusive, with a temperature independent (or weakly dependent) active diffusion coefficient.
Active transport and diffusion in cell membrane: We have studied the dynamics of particles advected in an active quasi-two-dimensional medium consisting of self-propelled filaments. We find that the particles show a tendency to cluster and non trivial density fluctuations. The late-time dynamics is diffusive, with a temperature independent (or weakly dependent) active diffusion coefficient.
See Phys. Rev. E 98 (5), 052608, (2018) for details.
Hydrodynamics of sheared complex fluids and its connection to rheochaos: We numerically analyse the full non-linear hydrodynamic equations of a sheared nematic fluid under shear stress and strain rate controlled situations. We show that in the chaotic regime the power spectra of stress and the injected power shows power law and the distribution of injected power is non-Gaussian and skewed, which bear striking resemblance to elastic turbulence phenomena observed in polymer solutions.
Hydrodynamics of sheared complex fluids and its connection to rheochaos: We numerically analyse the full non-linear hydrodynamic equations of a sheared nematic fluid under shear stress and strain rate controlled situations. We show that in the chaotic regime the power spectra of stress and the injected power shows power law and the distribution of injected power is non-Gaussian and skewed, which bear striking resemblance to elastic turbulence phenomena observed in polymer solutions.
See J. Phys.: Condens. Matter 32, 134002 (2019) for details.
Thermal transport in model glassy systems: We have shown that the thermal conductivity of a glass depends on its age and the cooling rate used in its preparation. We further show that the observed variation of the conductivity is linked with the properties of the underlying potential energy landscape: lower thermal conductivity corresponds to lower energy of the relevant inherent structures, which have more localized vibrational modes.
Thermal transport in model glassy systems: We have shown that the thermal conductivity of a glass depends on its age and the cooling rate used in its preparation. We further show that the observed variation of the conductivity is linked with the properties of the underlying potential energy landscape: lower thermal conductivity corresponds to lower energy of the relevant inherent structures, which have more localized vibrational modes.
See Phys. Rev. E 101, 022125 (2020) for details.
Dense Extreme Active Matter: It is a dense assembly of self-propelled particles with large persistence time τp and high Péclet number. As τp→0, it undergoes a gradual slowing down of density relaxations, as one reduces the active propulsion force f. In the limit, τp→∞, the fluid jams on lowering f, at a critical threshold f∗(∞). In between, the approach to dynamical arrest at low f, goes through a phase characterised by intermittency. Dense extreme active matter brings together the physics of glass, jamming, plasticity and turbulence, in a new state of driven classical matter.
Dense Extreme Active Matter: It is a dense assembly of self-propelled particles with large persistence time τp and high Péclet number. As τp→0, it undergoes a gradual slowing down of density relaxations, as one reduces the active propulsion force f. In the limit, τp→∞, the fluid jams on lowering f, at a critical threshold f∗(∞). In between, the approach to dynamical arrest at low f, goes through a phase characterised by intermittency. Dense extreme active matter brings together the physics of glass, jamming, plasticity and turbulence, in a new state of driven classical matter.
See Nat. Commun.,11, 2581 (2020) for details.
Multiple Types of Aging in Active Glass: Recent experiments and simulations have revealed glassy features in living matter and leads to a fundamental question: how do these active amorphous materials differ from conventional passive glasses? To address this we investigate the aging behaviour of a model active glass former. Using extensive molecular dynamics simulations we reveal rich aging behaviour of this dense active matter system.
Multiple Types of Aging in Active Glass: Recent experiments and simulations have revealed glassy features in living matter and leads to a fundamental question: how do these active amorphous materials differ from conventional passive glasses? To address this we investigate the aging behaviour of a model active glass former. Using extensive molecular dynamics simulations we reveal rich aging behaviour of this dense active matter system.
See Phys. Rev. Lett. 125, 218001(2020) for details.
How to Study a Persistent Active Glassy System? Numerical simulations of active glasses are computationally challenging when the dynamics is governed by large persistence times. We describe in detail a recently proposed scheme that allows one to study directly the dynamics in the large persistence time limit, on timescales both around and well above the persistence time. Specifically we consider dense assemblies of soft particles that are self-propelled by active forces. These forces have a fixed amplitude and a propulsion direction that varies on a timescale, the persistence timescale. We discuss the numerical implementation of the activity driven dynamics and establish that our prescription faithfully reproduces all dynamical quantities in the appropriate limit. We deploy the approach to explore in detail the statistics of the Eshelby-like plastic events in a dense and intermittent active super-cooled liquid.
How to Study a Persistent Active Glassy System? Numerical simulations of active glasses are computationally challenging when the dynamics is governed by large persistence times. We describe in detail a recently proposed scheme that allows one to study directly the dynamics in the large persistence time limit, on timescales both around and well above the persistence time. Specifically we consider dense assemblies of soft particles that are self-propelled by active forces. These forces have a fixed amplitude and a propulsion direction that varies on a timescale, the persistence timescale. We discuss the numerical implementation of the activity driven dynamics and establish that our prescription faithfully reproduces all dynamical quantities in the appropriate limit. We deploy the approach to explore in detail the statistics of the Eshelby-like plastic events in a dense and intermittent active super-cooled liquid.
See J. Phys.: Condens. Matter, 33, 184001 (2021) for details.
Interaction from Structure using Machine Learning: in and out of Equilibrium: Prediction of pair potential given a typical configuration of an interacting classical system is a difficult inverse problem. We demonstrate that using machine learning (ML) one can get a quick but accurate answer to the question:“which pair potential lead to the given structure ?” We show that this ML technique is capable of providing very accurate prediction of pair potential irrespective of whether the system is in a crystalline, liquid or gas phase and the trained network works well for sample system configurations taken from both equilibrium and out of equilibrium simulations (active matter systems). We show that the ML prediction about the effective interaction for the active system is not only useful to make prediction about the MIPS (motility induced phase separation) phase but also identifies the transition towards this state.
Interaction from Structure using Machine Learning: in and out of Equilibrium: Prediction of pair potential given a typical configuration of an interacting classical system is a difficult inverse problem. We demonstrate that using machine learning (ML) one can get a quick but accurate answer to the question:“which pair potential lead to the given structure ?” We show that this ML technique is capable of providing very accurate prediction of pair potential irrespective of whether the system is in a crystalline, liquid or gas phase and the trained network works well for sample system configurations taken from both equilibrium and out of equilibrium simulations (active matter systems). We show that the ML prediction about the effective interaction for the active system is not only useful to make prediction about the MIPS (motility induced phase separation) phase but also identifies the transition towards this state.
See Soft Matter, 17, 8322 (2021) for details.
Shear Induced Orientational Ordering in Active Glass: Using extensive molecular dynamics simulation and analytical theory we explore three dynamical steady states in a sheared model active glassy system: (a) a disordered, (b) a propulsion-induced ordered, and (c) a shear-induced ordered phase. Using the analytical description we make testable predictions for the joint distribution of single particle position and orientation and they match well with the joint distribution measured from direct numerical simulation.
Shear Induced Orientational Ordering in Active Glass: Using extensive molecular dynamics simulation and analytical theory we explore three dynamical steady states in a sheared model active glassy system: (a) a disordered, (b) a propulsion-induced ordered, and (c) a shear-induced ordered phase. Using the analytical description we make testable predictions for the joint distribution of single particle position and orientation and they match well with the joint distribution measured from direct numerical simulation.
See Proc. Natl. Acad. Sci. USA, 118 (39), (2021) for details.
The random first-order transition theory of active glass in the high-activity regime: An extension of the active -random first-order transition theory (ARFOT) was developed, whereby the activity was added to the free energy of the system in the form of the potential energy of an active particle, trapped by a effective harmonic potential. It predicts qualitative changes which agree with the results of simulations in both 3d and 2d models of active glass.
The random first-order transition theory of active glass in the high-activity regime: An extension of the active -random first-order transition theory (ARFOT) was developed, whereby the activity was added to the free energy of the system in the form of the potential energy of an active particle, trapped by a effective harmonic potential. It predicts qualitative changes which agree with the results of simulations in both 3d and 2d models of active glass.
See J. Phys. Commun. 6, 115001 (2022) for details.
Memory in Non-Monotonic Stress Response of an Athermal Disordered Solid: Athermal systems show a non-monotonic stress stress response (and memory effect) when decompressed somewhat after an initial compression, i.e. under a two-step, Kovacs-like protocol. In this work we use a model athermal jammed solid and recover identical phenomenology in the stress response under a two-step and three-step protocol and explain observed behaviour using Linear Response Theory.
Memory in Non-Monotonic Stress Response of an Athermal Disordered Solid: Athermal systems show a non-monotonic stress stress response (and memory effect) when decompressed somewhat after an initial compression, i.e. under a two-step, Kovacs-like protocol. In this work we use a model athermal jammed solid and recover identical phenomenology in the stress response under a two-step and three-step protocol and explain observed behaviour using Linear Response Theory.
See Phys. Rev. Research, 3, 043153 (2021), for details.
Stratification, multivalency and turnover of the active cortical machinery are required for steady active contractile flows at the cell surface: We develop a coarse-grained agent-based Brownian dynamics simulation that incorporates the effects of stratification, binding of myosin minifilaments to multiple actin filaments and their turnover. We show that stratification, multivalency and turnover are critical for the realisation of a nonequilibrium steady state characterised by contractile flows and dynamic orientational patterning and can facilitate multi-particle encounters of membrane proteins that profoundly influence the kinetics of bimolecular reactions.
Stratification, multivalency and turnover of the active cortical machinery are required for steady active contractile flows at the cell surface: We develop a coarse-grained agent-based Brownian dynamics simulation that incorporates the effects of stratification, binding of myosin minifilaments to multiple actin filaments and their turnover. We show that stratification, multivalency and turnover are critical for the realisation of a nonequilibrium steady state characterised by contractile flows and dynamic orientational patterning and can facilitate multi-particle encounters of membrane proteins that profoundly influence the kinetics of bimolecular reactions.
See https://arxiv.org/abs/2105.11358 (2021) for details.
Robust Prediction of Force Chains in Jammed Solids using Graph Neural Networks: Force chains are ubiquitous in jammed amorphous materials, such as granular materials, foams, emulsions or even assemblies of cells. Here we demonstrate that graph neural networks (GNN) can accurately infer the location of these force chains in frictionless materials from the local structure prior to deformation, without receiving any information about the inter-particle forces. The GNN prediction accuracy proves to be robust to changes in packing fraction, mixture composition, amount of deformation, and the form of the interaction potential.
Robust Prediction of Force Chains in Jammed Solids using Graph Neural Networks: Force chains are ubiquitous in jammed amorphous materials, such as granular materials, foams, emulsions or even assemblies of cells. Here we demonstrate that graph neural networks (GNN) can accurately infer the location of these force chains in frictionless materials from the local structure prior to deformation, without receiving any information about the inter-particle forces. The GNN prediction accuracy proves to be robust to changes in packing fraction, mixture composition, amount of deformation, and the form of the interaction potential.
Unjamming and emergent nonreciprocity in active ploughing through a compressible viscoelastic fluid: The compressible viscoelastic medium is actively churned up by few active brownian particles: (a) the active particle gets self-trapped in a spherical cavity (small persistence), (b) the active particle ploughs through the medium (large persistence), leading to a moving anisotropic wake, or leaving a porous trail. Using a a hydrodynamic approach we show that the active particle generates a long range density wake which breaks fore-aft symmetry, consistent with the simulations and also describe (i) dynamical jamming of the active particles, and (ii) a dynamical non-reciprocal attraction between two active particles moving along the same direction.
Unjamming and emergent nonreciprocity in active ploughing through a compressible viscoelastic fluid: The compressible viscoelastic medium is actively churned up by few active brownian particles: (a) the active particle gets self-trapped in a spherical cavity (small persistence), (b) the active particle ploughs through the medium (large persistence), leading to a moving anisotropic wake, or leaving a porous trail. Using a a hydrodynamic approach we show that the active particle generates a long range density wake which breaks fore-aft symmetry, consistent with the simulations and also describe (i) dynamical jamming of the active particles, and (ii) a dynamical non-reciprocal attraction between two active particles moving along the same direction.
Mean field description of aging linear response in athermal amorphous solids: We study the linear response to strain in a mean field elastoplastic model for athermal amorphous solids, incorporating the power-law mechanical noise spectrum arising from plastic events. We determine the scaling behaviour of this aging linear response analytically where the scaling arising from the stretched exponential decay of the plastic activity. We compare these predictions with measurements of the linear response in computer simulations of a model jammed system of repulsive soft athermal particles and find good agreement with the theory.
Mean field description of aging linear response in athermal amorphous solids: We study the linear response to strain in a mean field elastoplastic model for athermal amorphous solids, incorporating the power-law mechanical noise spectrum arising from plastic events. We determine the scaling behaviour of this aging linear response analytically where the scaling arising from the stretched exponential decay of the plastic activity. We compare these predictions with measurements of the linear response in computer simulations of a model jammed system of repulsive soft athermal particles and find good agreement with the theory.