arXiv subjects

Amruthesh Thirumalaiswamy

Publications and source records attributed to Amruthesh Thirumalaiswamy.

4 recordsLinked to original sources

arXiv2026

Streaming Molecular Dynamics Simulation Data for On-the-fly Processing and Analysis

Only a small fraction of the data generated in state-of-the-art all-atom multi-microsecond molecular dynamics (MD) simulations is typically analyzed. With femtosecond integration steps, microsecond simulations generate billions of time steps containing atomic positions, velocities, and forces, often corresponding to petabytes of data. Since this exceeds practical storage capacities, only a subset of frames is usually written to trajectory files at intervals of 10-100 ps. While sufficient for ensemble averages and slow dynamics, this approach discards information on fast processes such as molecular vibrations, solvent dynamics, short-lived transition states, and transport phenomena that are directly related to macroscopic properties and experimental observables. Here, we introduce a streaming framework for MD simulations that provides direct access to all data generated during a running simulation. Instead of writing high-frequency output to disk, the framework enables user-defined analysis and processing routines to access simulation data in real time. To achieve this, we extend the Interactive Molecular Dynamics (IMD) protocol and implement an enhanced version, termed IMDv3, in the MD packages GROMACS, NAMD, and LAMMPS. We further introduce the Python package imdclient, which receives IMDv3 data streams and exposes them to external applications. To maximize usability, we add an imdclient-based reader to the MDAnalysis package, enabling streamed simulation data to be analyzed alongside conventional trajectory files. Benchmark results show that streaming can improve performance compared to simulations with high-frequency trajectory output. Example applications include live monitoring of custom variables, evaluation of velocity time-correlation functions with fast fluctuations, and real-time analysis of membrane pore currents.

cond-mat.stat-mech↗

arXiv2025

Slow Relaxation and Landscape-Driven Dynamics in Viscous Ripening Foams

Foams and dense emulsions display complex mechanical behavior, including intermittent rearrangement dynamics, power-law rheology, and slow recovery after perturbation. These effects have long been considered evidence for glassy physics in these and other materials having similar mechanics, such as the cytoskeleton. Here we study such anomalous mechanics in a simulated wet foam driven by ripening and find behavior that has a different physical origin than that in glasses. Rather, the dynamics is due to a balance of forces from the system's self-similar potential energy landscape and viscous stress. At the lowest viscosities, bubbles move intermittently, with the system shifting abruptly between shallow potential energy minima. For higher viscosities, in contrast, the bubbles move continuously and the system follows a tortuous, fractal path through high-dimensional configuration space, at higher mean energy than the lower viscosity case. The long-time dynamics and power-law rheology are the direct consequence of the potential energy landscape's self-similar geometry. Lastly, we find that the slow recovery of perturbed foams is due to the foam being kinetically rather than energetically trapped in high-energy portions of the energy landscape. Overall, viscous ripening foams follow a biased energy minimization pathway that explores regions of the energy landscape that are qualitatively different (flatter and smoother) than those corresponding to well-annealed glasses.

cond-mat.soft↗

arXiv2023

Experimental observations of fractal landscape dynamics in a dense emulsion

Many soft and biological materials display so-called 'soft glassy' dynamics; their constituents undergo anomalous random motions and complex cooperative rearrangements. A recent simulation model of one soft glassy material, a coarsening foam, suggested that the random motions of its bubbles are due to the system configuration moving over a fractal energy landscape in high-dimensional space. Here we show that the salient geometrical features of such high-dimensional fractal landscapes can be explored and reliably quantified, using empirical trajectory data from many degrees of freedom, in a model-free manner. For a mayonnaise-like dense emulsion, analysis of the observed trajectories of oil droplets quantitatively reproduces the high-dimensional fractal geometry of the configuration path and its associated energy minima generated using a computational model. That geometry in turn drives the droplets' complex random motion observed in real space. Our results indicate that experimental studies can elucidate whether the similar dynamics in different soft and biological materials may also be due to fractal landscape dynamics.

cond-mat.soft↗

arXiv2022

Exploring canyons in glassy energy landscapes using metadynamics

The complex physics of glass forming systems is controlled by the structure of the low energy portions of their potential energy landscapes. Here, we report that a modified metadynamics algorithm efficiently explores and samples low energy regions of such high-dimensional landscapes. In the energy landscape for a model foam, our algorithm finds and descends meandering `canyons' in the landscape, which contain dense clusters of energy minima along their floors. Similar canyon structures in the energy landscapes of two model glass formers -- hard sphere fluids and the Kob-Andersen glass -- allow us to reach high densities and low energies, respectively. In the hard sphere system, fluid configurations are found to form continuous regions that cover the canyon floors up to densities well above the jamming transition. For the Kob-Andersen glass former, our technique samples low energy states with modest computational effort, with the lowest energies found approaching the predicted Kauzmann limit.

cond-mat.soft↗