Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.24047

Multiple propagating modes in random polycrystals

Ming Huang·Stanislav I. Rokhlin·Michael J. S. Lowe
View original source ↗Read source document ↗Export metadata ↓

Abstract

Multiple-scattering theory predicts that the coherent elastic wave in a statistically isotropic polycrystal is not a single wave. Its dispersion equation admits multiple roots of a given polarization, but these extra modes have never been observed. Here, using grain-scale finite-element simulations of three-dimensional polycrystals, we resolve the coherent field in the frequency--wavenumber plane and directly observe the predicted multiplicity for both transverse and longitudinal excitations: a single mode at low frequencies gives way to two in the geometric regime as the wavelength approaches the grain correlation length. The measured modes follow the roots of the dispersion equation, which always carries a third root whose attenuation exceeds that of the others. It is this attenuation hierarchy, not the root count, that dictates how many modes propagate and can be observed. Coherent wave propagation in random media is thus inherently multimodal in the geometric regime, which we reach here for the first time in polycrystals.

Explore related subjects

cond-mat.mtrl-sciphysics.app-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ming Huang, Stanislav I. Rokhlin, Michael J. S. Lowe. 2026-10-16. Multiple propagating modes in random polycrystals. https://arxiv.org/abs/2610.24047

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Diversity-Aware Active Learning Builds Materials Datasets for Current and Future Objectives

Materials science data collection can be expensive, making dataset reuse and long-term utility important for future discovery. In practice, data collection often prioritizes properties of current interest while other properties are excluded from the acquisition process. This can produce datasets that are well suited for current objectives but lose information needed for properties of future interest. Here, we present how to construct datasets that remain informative for both. We develop and evaluate acquisition frameworks that combine predictive disagreement with diversity in material features, predicted outcomes, or both. We test these approaches across four DFT datasets and an experimental thermoelectric dataset. Diversity inclusion in dataset construction can reduce present and future objectives trade-off by preserving a broader range of materials and property behavior during acquisition. Across all datasets, the Non-dominated Sorting Genetic Algorithm II with disagreements and feature diversity (NSGA-II Dis. + diversity) framework developed in this work provides the most robust balance between current and future objectives. It maintains prediction performance above random sampling for current objectives while mitigating bias on excluded properties and, in many cases, improving their performance beyond random sampling. This diversity-aware acquisition framework provides a practical route to building materials datasets with unbiased quality entries and mitigating cold-start limitations in subsequent modeling and discovery campaigns.

cond-mat.mtrl-sci↗
arXiv2026

Bias Tunable Transport Modulation and Gas Selectivity in Layered BiOI: A DFT NEGF Study

Understanding the interplay between adsorption energetics and charge-transport modulation is essential for the rational design of low-power and bias-tunable gas sensors. Here, we present a comprehensive first-principles study of gas selectivity in layered bismuth oxyiodide (BiOI) by integrating density functional theory with nonequilibrium Green's function transport calculations. The adsorption and bias-dependent transport responses toward NO2, NH3, CO2, and representative volatile organic compounds are systematically examined. While NH3 and NO2 exhibit strong chemisorption and localized electronic perturbations, CO2 interacts through weak physisorption, demonstrating that adsorption strength alone does not determine sensing performance. Instead, the evolution of transmission channels near the Fermi level governs the sensing response. Bias-dependent calculations reveal an electrically tunable sensitivity hierarchy, in which weakly adsorbed CO2 preserves conductive pathways and exhibits pronounced low-bias sensitivity despite minimal charge transfer. Recovery-time analysis further highlights the trade-off between transport modulation and reversibility for strongly adsorbed species. These results establish a transport-centered selectivity framework for layered BiOI and provide mechanistic insight into electric-field-controlled gas sensing under ambient conditions.

cond-mat.mtrl-sci↗
arXiv2026

Deep Generative Crystal Structure Prediction: A Benchmark Study and a Controlled Test of Prototype Dependence

Deep generative models are widely reported to enable de novo crystal structure prediction (CSP), but their capability has not been measured consistently against template-based methods. We evaluate 12 representative generative CSP models, spanning latent-variable, diffusion, flow-matching, autoregressive, and manifold random-walk architectures, against TCSP 2.0 on 180 test structures and a leakage-controlled subset of 46. All methods use identical structure-matching, symmetry, and consensus criteria. Template retrieval is the strongest single method, reaching 68.3% top-1 success; symmetry-aware EquiCSP (66.4%) and Uni-3DAR (62.9%) form the next tier. However, comparison with TCSP 2.0 shows that most structures correctly predicted by generative models are also correctly predicted by template substitution. Thus, the set of structures uniquely reachable by generation is small, limiting its practical advantage for discovering structures outside existing prototype libraries. To test the source of this performance, we removed entire stoichiometric prototype families from the training set and retrained the strongest generative model. Accuracy declined by 50-78% across four families, establishing that performance is substantially prototype-dependent. A small minority of structures survived removal of their prototype family, demonstrating a real but limited retrieval-independent predictive capacity. Present generative CSP models therefore function largely as implicit, softer-edged prototype libraries rather than genuinely de novo predictors. Enlarging this residual capacity, rather than aggregate match rate alone, is the central open problem.

cond-mat.mtrl-sci↗