Search

In this work, we introduce GauSim, a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels. Unlike traditional methods that treat kernels as particles within particle-based simulations, we leverage continuum mechanics, modeling each kernel as a continuous piece of matter to account for realistic deformations without idealized assumptions. To improve computational efficiency and fidelity, we employ a hierarchical structure that organizes kernels into Center of Mass Systems (CMS) with explicit formulations, enabling a coarse-to-fine simulation approach. This structure significantly reduces computational overhead while preserving detailed dynamics. In addition, GauSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations. To validate our approach, we present a new dataset, READY, containing multi-view videos of real-world elastic deformations. Experimental results demonstrate that GauSim achieves superior performance compared to existing physics-driven baselines, offering a practical and accurate solution for simulating complex dynamic behaviors. Code and model will be released. Project page: https://www.mmlab-ntu.com/project/gausim/index.html ., Comment: Project page: https://www.mmlab-ntu.com/project/gausim/index.html

Implicit Discourse Relation Recognition (IDRR), which infers discourse relations without the help of explicit connectives, is still a crucial and challenging task for discourse parsing. Recent works tend to exploit the hierarchical structure information from the annotated senses, which demonstrate enhanced discourse relation representations can be obtained by integrating sense hierarchy. Nevertheless, the performance and robustness for IDRR are significantly constrained by the availability of annotated data. Fortunately, there is a wealth of unannotated utterances with explicit connectives, that can be utilized to acquire enriched discourse relation features. In light of such motivation, we propose a Prompt-based Logical Semantics Enhancement (PLSE) method for IDRR. Essentially, our method seamlessly injects knowledge relevant to discourse relation into pre-trained language models through prompt-based connective prediction. Furthermore, considering the prompt-based connective prediction exhibits local dependencies due to the deficiency of masked language model (MLM) in capturing global semantics, we design a novel self-supervised learning objective based on mutual information maximization to derive enhanced representations of logical semantics for IDRR. Experimental results on PDTB 2.0 and CoNLL16 datasets demonstrate that our method achieves outstanding and consistent performance against the current state-of-the-art models., Comment: This paper is accepted by the EMNLP 2023 Main Conference

The dynamics of a spherical chemically-powered synthetic colloidal motor that operates by a self-diffusiophoretic mechanism and has a catalytic domain of arbitrary shape is studied using both continuum theory and particle-based simulations. The motor executes active rotational motion when self-generated concentration gradients and interactions between the chemical species and colloidal motor surface break spherical symmetry. Local variations of chemical reaction rates on the motor catalytic surface with catalytic domain sizes and shapes provide such broken symmetry conditions. A continuum theoretical description of the active rotational motion is given, along with the results of particle-based simulations of the active dynamics. From these results a detailed description of the factors responsible for the active rotational dynamics can be given. Since active rotational motion often plays a significant part in the nature of the collective dynamics of many-motor systems and can be used to control motor motion in targeted cargo transport, our results should find applications beyond those considered here., Comment: 11 pages, 7 figures, 1 table

The single-particle and collective dynamics of systems comprising Janus motors, solvent and reactive solute species maintained in nonequilibrium states are investigated. Reversible catalytic reactions with the solute species take place on the catalytic faces of the motors, and the nonequilibrium states are established by imposing either constant-concentration reservoirs that feed and remove reactive species, or through out-of-equilibrium fluid phase reactions. We consider general intermolecular interactions between the Janus motor hemispheres and the reactive species. For single motors, we show that the reaction rate depends nonlinearly on an applied external force when the system is displaced far from equilibrium. We also show that a finite-time fluctuation formula derived for fixed catalytic particles describes the nonequilibrium reactive fluctuations of moving Janus motors. Simulation of the collective dynamics of small ensembles of Janus motors with reversible kinetics under nonequilibrium conditions are carried out and the spatial and orientational correlations of dynamic cluster states are discussed. The conditions leading to the instability of the homogeneous motor distribution and the onset of nonequilibrium dynamical clustering are described.

A finite-time fluctuation theorem for the diffusion-influenced surface reaction A <=> B is investigated for spherical and Janus catalytic particles. The finite-time rates and thermodynamic force are analytically calculated by solving diffusion equations with the special boundary conditions of the finite-time fluctuation theorem. Theory is compared with numerical simulations carried out with two different methods: a random walk algorithm and multiparticle collision dynamics.

Janus motors with chemically active and inactive hemispheres can operate only under nonequilibrium conditions where detailed balance is broken by fluxes of chemical species that establish a nonequilibrium state. A microscopic model for reversible reactive collisions on a Janus motor surface is constructed and shown to satisfy detailed balance. The model is used to study Janus particle reactive dynamics in systems at equilibrium where generalized chemical rate laws that include time-dependent rate coefficients with power-law behavior are shown to describe reaction rates. While maintaining reversible reactions on the Janus catalytic hemisphere, the system is then driven into a nonequilibrium steady state by fluxes of chemical species that control the chemical affinity. The statistical properties of the self-propelled Janus motor in this nonequilibrium steady state are investigated and compared with predictions of a fluctuating thermodynamics theory. The model has utility beyond the examples presented here, since it allows one to explore various aspects of nonequilibrium fluctuations in systems with self-diffusiophoretic motors from a microscopic perspective.

Collective motion in nonequilibrium steady state suspensions of self-propelled Janus motors driven by chemical reactions can arise due to interactions coming from direct intermolecular forces, hydrodynamic flow effects, or chemotactic effects mediated by chemical gradients. The relative importance of these interactions depends on the reactive characteristics of the motors, the way in which the system is maintained in a steady state, and properties of the suspension, such as the volume fraction. From simulations of a microscopic hard collision model for the interaction of fluid particles with the Janus motor we show that dynamic cluster states exist and determine the interaction mechanisms that are responsible for their formation. The relative importance of chemotactic and hydrodynamic effects is identified by considering a microscopic model in which chemotactic effects are turned off while the full hydrodynamic interactions are retained. The system is maintained in a steady state by means of a bulk reaction in which product particles are reconverted into fuel particles. The influence of the bulk reaction rate on the collective dynamics is also studied.

Active media, whose constituents are able to move autonomously, display novel features that differ from those of equilibrium systems. In addition to naturally-occurring active systems such as populations of swimming bacteria, active systems of synthetic self-propelled nanomotors have been developed. These synthetic systems are interesting because of their potential applications in a variety of fields. Janus particles, synthetic motors of spherical geometry with one hemisphere that catalyzes the conversion of fuel to product and one noncatalytic hemisphere, can propel themselves in solution by self-diffusiophoresis. In this mechanism the concentration gradient generated by the asymmetric catalytic activity leads to a force on the motor that induces fluid flows in the surrounding medium. These fluid flows are studied in detail through microscopic simulations of Janus motor motion and continuum theory. It is shown that continuum theory is able to capture many but not all features of the dynamics of the Janus motor and the velocity fields of the fluid.

The RMPCDMD software package performs hybrid Molecular Dynamics simulations, coupling Multiparticle Collision Dynamics to model the solvent and Molecular Dynamics to model suspended colloids, including hydrodynamics, thermal fluctuations, and chemically active solvent particles and catalytic colloids. The main usage of RMPCDMD is the simulation of chemically powered nanomotors, but other setups are considered: colloids in the presence of a thermal gradients or forced flows. RMPCDMD is developed in Fortran 2008 with OpenMP for multithreaded operation and uses the HDF5-based H5MD file format for storing data. RMPCDMD comes with documentation and a tutorial for the simulation of chemically powered nanomotors., Comment: 7 pages

Very small synthetic motors that make use of chemical reactions to propel themselves in solution hold promise for new applications in the development of new materials, science and medicine. The prospect of such potential applications, along with the fact that systems with many motors or active elements display interesting cooperative phenomena of fundamental interest, has made the study of synthetic motors an active research area. Janus motors, comprising catalytic and noncatalytic hemispheres, figure prominently in experimental and theoretical studies of these systems. While continuum models of Janus particle systems are often used to describe motor dynamics, microscopic models that are able to account for intermolecular interactions, many-body concentration gradients, fluid flows and thermal fluctuations provide a way to explore the dynamical behavior of these complex out-of-equilibrium systems that does not rely on approximations that are often made in continuum theories. The analysis of microscopic models from first principles provides a foundation from which the range of validity and limitations of approximate theories of the dynamics may be assessed. In this paper, a microscopic model for the diffusiophoretic propulsion of Janus motors, where motor interactions with the environment occur only through hard collisions, is constructed, analyzed and compared to theoretical predictions. Microscopic simulations of both single-motor and many-motor systems are carried out to illustrate the results.