4025 papers
Condensed matter physics and materials science form a dynamic partnership, exploring how the collective behavior of atoms gives rise to the unique properties of solids and liquids. This field bridges the gap between fundamental quantum mechanics and the practical engineering of everything from flexible electronics to superconductors, turning abstract theories into tangible innovations that shape our daily lives.
At Gist.Science, we process every new preprint in this category directly from arXiv to make these complex discoveries accessible to everyone. Our team generates both plain-language overviews and detailed technical summaries for each paper, ensuring that researchers, students, and curious minds alike can grasp the latest breakthroughs without getting lost in dense jargon.
Below are the latest papers in condensed matter and materials science, organized by their most recent publication dates.
This paper proposes answer-conditioned chain-of-thought distillation to rapidly adapt small vision-language models to few-shot industrial visual inspection tasks, demonstrating that generating reasoning conditioned on correct labels significantly outperforms direct fine-tuning and even larger models like GPT-4.1 when trained on minimal data.
This paper introduces a simplified cluster-based model that accurately describes the optical spectrum of erbium-doped gadolinium vanadate by accounting for interactions between the erbium ion and its nearest-neighbor gadolinium ions, offering a physically transparent framework for predicting microwave-to-optical transduction.
This paper presents a neural-network-based approach that accurately reconstructs spin and orbital angular momentum directly from full X-ray magnetic circular dichroism spectral line shapes, offering a data-driven alternative to conventional sum-rule analyses that rely on integrated intensities.
This study investigates the temperature dependence of charge-to-spin conversion in epitaxial rhombohedral (110) bismuth films, revealing that the observed enhancement of spin Hall conductivity and spin diffusion length at lower temperatures indicates that spin scattering is dominated by the Elliott-Yafet mechanism while the conversion itself is primarily driven by skew scattering.
This paper introduces an uncertainty-aware framework using heteroscedastic Gaussian process regression to model the stochastic structure-property relationships of spinodoid metamaterials, demonstrating that accounting for morphology-induced variability is essential for achieving reliable design optimization compared to traditional deterministic or homoscedastic approaches.
This paper introduces PAC Studio ML, a human-in-the-loop Python desktop environment that integrates machine learning with physics-informed modeling to accelerate and enhance the expert analysis of Time-differential Perturbed Angular Correlation (TDPAC) spectra by providing robust parameter initialization, site-count hypothesis testing, and improved reproducibility without replacing conventional expert interpretation.
This paper introduces a calendering-aware extension of the Zehner–Bauer–Schlunder model that successfully captures the non-monotonic, U-shaped evolution of through-plane thermal conductivity in lithium-ion electrodes by accounting for process-dependent microstructural changes, thereby reducing prediction error from 31.1% to 4.5% across various graphite and NMC formulations.
This study combines experimental molten salt etching of Ti2InB2 with ZnCl2 and density functional theory calculations to successfully synthesize and characterize multilayer Ti2B2Clx MBenes, demonstrating a direct biphasic transformation and promising initial performance in Li-ion batteries.
This paper employs fracture mechanics and finite element simulations to derive analytical expressions explaining how 3M Command strips achieve high load-bearing capacity and clean removal by leveraging the ratio of bonded length to adhesive thickness and controlling interfacial crack propagation through hyperelastic deformation.
This paper introduces a material-constrained atomic optimization (MCAO) framework to generate numerically stable, correlation-consistent Gaussian basis sets for copper solids, enabling reliable complete-basis-set benchmarks for bulk properties and CO adsorption while overcoming linear dependence issues inherent in standard atomically optimized sets.