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Register a new userOut-of-equilibrium versus dynamical and thermodynamical transitions for a model protein
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Equilibrium and out-of-equilibrium transitions of an off-lattice protein model have been identified and studied. In particular, the out-of-equilibrium dynamics of the protein undergoing mechanical unfolding is investigated, and by using a work fluctuation relation, the system free energy landscape is evaluated. Three different structural transitions are identified along the unfolding pathways. Furthermore, the reconstruction of the the free and potential energy profiles in terms of inherent structure formalism allows us to put in direct correspondence these transitions with the equilibrium thermal transitions relevant for protein folding/unfolding. Through the study of the fluctuations of the protein structure at different temperatures, we identify the dynamical transitions, related to configurational rearrangements of the protein, which are precursors of the thermal transitions.
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Non-equilibrium aspects of the BCS model have fascinated physicists for decades, from the seminal works of Eliashberg to modern realizations in cold atom experiments. The latter scenarios have lead to a great deal of interest in the quench dynamics of fermions with pairing interactions. The recently introduced notion of a dynamical quantum phase transition is an attempt to classify the myriad of possible phenomena which can result in such far from equilibrium systems. These are defined as non-analytic points of the logarithm of the Loschmidt echo and are linked to oscillations in the dynamics a systems order parameter. In this work we analytically investigate the relation between DQPTs and oscillation of the superconducting order parameter in quenches of the BCS model. We find that each oscillation of the order parameter is accompanied by a DQPT which is first order in nature. We show this for a variety of initial states and furthermore find that when the order parameter attains a constant steady state then no DQPTS occur.
Prediction of pair potential given a typical configuration of an interacting classical system is a difficult inverse problem. There exists no exact result that can predict the potential given the structural information. 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 (represented by pair correlation function)? We use artificial neural network (NN) to address this question and 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. We show that the trained network works well for sample system configurations taken from both equilibrium and out of equilibrium simulations (active matter systems) when the later is mapped to an effective equilibrium system with a modified potential. 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.
In this work we compare and characterize the behavior of Langevin and Dissipative Particle Dynamics (DPD) thermostats in a broad range of non-equilibrium simulations of polymeric systems. Polymer brushes in relative sliding motion, polymeric liquids in Poiseuille and Couette flows, and brush-melt interfaces are used as model systems to analyze the efficiency and limitations of different Langevin and DPD thermostat implementations. Widely used coarse-grained bead-spring models under good and poor solvent conditions are employed to assess the effects of the thermostats. We considered equilibrium, transient, and steady state examples for testing the ability of the thermostats to maintain constant temperature and to reproduce the underlying physical phenomena in non-equilibrium situations. The common practice of switching-off the Langevin thermostat in the flow direction is also critically revisited. The efficiency of different weight functions for the DPD thermostat is quantitatively analyzed as a function of the solvent quality and the non-equilibrium situation.
Experiments in bulk water confirm the existence of two local arrangements of water molecules with different densities, but, because of inevitable freezing at low temperature $T$, can not ascertain whether the two arrangements separate in two phases. To avoid the freezing, new experiments measure the dynamics of water at low $T$ on the surface of proteins, finding a crossover from a non-Arrhenius regime at high $T$ to a regime that is approximately Arrhenius at low $T$. Motivated by these experiments, Kumar et al. [Phys. Rev. Lett. 100, 105701 (2008)] investigated, by Monte Carlo simulations and mean field calculations, the relation of the dynamic crossover with the coexistence of two liquid phases in a cell model for water and predict that: (i) the dynamic crossover is isochronic, i.e. the value of the crossover time $tau_{rm L}$ is approximately independent of pressure $P$; (ii) the Arrhenius activation energy $E_{rm A}(P)$ of the low-$T$ regime decreases upon increasing $P$; (iii) the temperature $T^*(P)$ at which $tau$ reaches a fixed macroscopic time $tau^*geq tau_{rm L}$ decreases upon increasing $P$; in particular, this is true also for the crossover temperature $T_{rm L}(P)$ at which $tau=tau_{rm L}$. Here, we compare these predictions with recent quasi elastic neutron scattering (QENS) experiments performed by X.-Q. Chu {it et al.} on hydrated proteins at different values of $P$. We find that the experiments are consistent with these three predictions.
Biological activity gives rise to non-equilibrium fluctuations in the cytoplasm of cells; however, there are few methods to directly measure these fluctuations. Using a reconstituted actin cytoskeleton, we show that the bending dynamics of embedded microtubules can be used to probe local stress fluctuations. We add myosin motors that drive the network out of equilibrium, resulting in an increased amplitude and modified time-dependence of microtubule bending fluctuations. We show that this behavior results from step-like forces on the order of 10 pN driven by collective motor dynamics.
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