43 papers
Atmospheric clustering explores how tiny particles in the air group together to form clouds, fog, and even influence our weather patterns. This fascinating intersection of physics and meteorology reveals the invisible dance of molecules that shapes everything from a gentle breeze to a massive storm system. Understanding these microscopic interactions is key to predicting climate change and improving air quality forecasts for communities worldwide.
On Gist.Science, we track every new preprint published in the atmospheric clustering category on arXiv. Our team processes each submission to provide both a clear, plain-language explanation and a detailed technical summary, ensuring that complex research is accessible to students, policymakers, and curious minds alike.
Below are the latest papers in atmospheric clustering, updated daily directly from the source.
This study utilizes in-situ transmission electron diffraction to demonstrate that large, substrate-free Ar-Kr clusters formed via supersonic expansion exhibit a size-dependent coexistence of fcc and hcp phases, with the hexagonal fraction increasing with cluster size and peaking at equimolar composition, supporting a thermally activated diffusion mechanism for hcp nucleation.
This study employs time-dependent wave-packet propagation to demonstrate that including a large manifold of resonant states and rotational couplings significantly enhances the dissociative recombination and resonant ion-pair formation cross sections for isotopologues, thereby highlighting the critical role of multi-state nonadiabatic effects in accurately modeling electron-molecule collisions in astrophysical plasmas.
This paper demonstrates that "rainbow RABBITT," a technique analyzing intra-sideband phase dispersion in attosecond pulse train spectra, serves as a sensitive interferometric probe for mapping coherent Rabi dynamics and distinguishing between resonant and detuned dressed states, revealing that exact resonance flattens phase dispersion while small detuning induces pronounced modulation contrary to population transfer expectations.
This study evaluates the impact of various DFT functionals and basis sets on the electronic structure of nitric oxide to determine their influence on low-energy electron scattering cross sections, ultimately recommending the B97X-D3/aug-cc-pVTZ geometry optimization followed by aug-cc-pVQZ property calculations as the most practical protocol for R-matrix modeling.
This paper establishes a general theoretical framework demonstrating that molecular chirality in dimerized dipolar arrays induces tunable topological edge states with unique stereochemical labeling, offering a controllable platform for quasi-one-dimensional topological quantum matter.
By combining dispersion-corrected density functional theory with physics-informed machine learning, this study elucidates the sulfur adsorption and poisoning mechanisms across 30 transition metal 13-atom icosahedral clusters, identifying the Ti-Zr-Hf isoelectronic triad as a balanced group for designing sulfur-tolerant subnanometer catalysts.
This paper introduces RADII, a comprehensive benchmark of ~75,000 crystal-derived nanoparticle structures that systematically characterizes the "extrapolation frontier" of graph generative models by revealing how their structural fidelity degrades as they generate materials beyond their training size, thereby establishing output scale as a critical evaluation axis for materials discovery.
This paper proposes a novel active learning framework for machine-learned coarse-grained molecular dynamics that dynamically queries all-atom data during simulation to correct model degradation in under-sampled conformational regions, resulting in a 33.05% improvement in Wasserstein-1 metrics for the Chignolin protein.
This study establishes the FeDy molecular ring as a viable platform for the direct preparation, accumulation, and detection of finite-temperature toroidal polarization by combining an ab initio-informed theoretical framework with a novel protocol utilizing temporally asymmetric near-infrared waveforms and magnetoelectric coupling.
This paper introduces ChatMOSP, a chemistry-grounded mobile agent that translates natural language requests into validated multiscale simulations of working-state catalysts by dynamically mapping reaction conditions to morphology and activity models, retrieving necessary parameters from databases or literature, and successfully replicating complex experimental phenomena like temperature-induced morphological transitions and oscillatory reaction behaviors.