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Rachna Ramesh

Publications and source records attributed to Rachna Ramesh.

2 recordsLinked to original sources

A Dataset of Equilibrium State Configurations of Adsorption in Zeolites

Zeolites are crystalline nanoporous materials widely used in adsorption, separation, and catalytic processes. Molecular simulations are commonly used to predict adsorption properties, but most high-throughput adsorption datasets report only ensemble-averaged quantities such as loadings or isotherms, rather than the molecular configurations from which these averages are obtained. Here, we present AdsZeo, a coordinate-resolved dataset of equilibrium methane adsorption configurations in aluminium-substituted, sodium-containing zeolite frameworks. The processed release contains 4,775 framework realisations derived from 191 zeolite topologies. Each framework realisation was simulated at 13 methane pressures between 0.1 and 100 bar at 298 K using grand canonical Monte Carlo simulations, giving 62,075 production simulations in total. In addition to scalar adsorption records, the dataset stores production-frame methane pseudo-atom coordinates, mobile Na$^+$ cation coordinates, framework atomic coordinates, per-frame loading and energy statistics, and simulation metadata in a processed DuckDB database. The release contains 12,415,000 saved production-frame records and 1,245,376,215 saved particle-coordinate records. AdsZeo provides coordinate-resolved adsorption data across variations in framework topology, aluminium content and distribution, sodium cation arrangement, pressure, and methane loading, enabling reuse for adsorption analysis, spatial statistics, density estimation, and machine-learning models for molecular configuration generation in charged zeolite pores.

cond-mat.mtrl-sci

BubbleSH: A Dataset of Rising Bubbles with Deformable Interfaces

Bubbly flows exhibit complex multiscale dynamics, with deformable bubbles interacting through the surrounding liquid and giving rise to strongly coupled kinematic and morphological behavior. We present BubbleSH, a bubbly flows dataset consisting of transient, three-dimensional bubble-swarm dynamics obtained from high-fidelity direct numerical simulations of bubbles rising in a periodic domain. The dataset provides time-resolved bubble trajectories, velocities, and shape evolution, with bubble morphology compactly represented using spherical harmonics. Designed to be lightweight yet physically expressive, the dataset enables data-driven modeling of bubbly flow simulators where shape deformation and bubble-bubble interactions play a central role. We characterize the dataset with bubble kinematics, morphology, and interaction patterns, and introduce evaluation metrics for both trajectory and shape prediction. The sensitivity of bubble-swarm dynamics to local perturbations makes BubbleSH particularly well suited to generative models that learn distributions over possible future trajectories. We evaluate a permutationally and translationally equivariant probabilistic emulator on BubbleSH given the proposed metrics. Therefore, we establish a compact, high-fidelity dataset and a benchmark for developing and evaluating data-driven models of deformable, chaotic multiphase systems.

cs.LG