2806 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 demonstrates that lanthanide nanocrystals can be driven into a non-Boltzmann steady state via mid-infrared irradiation to achieve ultra-low-power, ratiometric detection and room-temperature imaging across the 6.8–8.6 μm spectrum, overcoming the thermal equilibrium limitations of conventional approaches.
This paper proposes a magnon-driven anomalous Hall effect in altermagnets, demonstrating that the coherent excitation of chiral magnons induces a Hall conductivity determined solely by the chirality of Néel-order precession, thereby enabling a distinct transport phenomenon that can exist even when the static anomalous Hall effect is symmetry-forbidden.
This paper proposes a theoretical framework using inelastic x-ray scattering to isolate and characterize the internal charge distribution of optically pumped Wannier excitons in transition metal dichalcogenides by analyzing differential scattering spectra.
This study demonstrates that Differential Scanning Calorimetry (DSC) curves serve as direct fingerprints for predicting the mechanical properties of aluminum alloys, showing that machine learning models trained on thermal data can accurately estimate yield strength, tensile strength, and elongation while identifying specific precipitation-related temperature regions as key predictive features.
This paper presents a thermodynamically consistent finite element framework to quantify the competing and synergistic effects of thermomigration and stress-driven transport on hydrogen redistribution in metals, demonstrating that while thermomigration often dominates in heat-carrying components, stress-driven transport becomes decisive near sharp stress concentrators, thereby offering practical guidance for assessing embrittlement risks.
This paper proposes a Griffith's theory-based analytical model for alkali metal dendrite growth in ceramic solid electrolytes, demonstrating that the critical current density scales with the 3/2 power of the longest pre-existing interfacial defect and follows a Weibull distribution due to the principle of minimal power dissipation.
This paper introduces a Transformer-based multi-target surrogate model that achieves DFT-level accuracy in predicting the electronic and phonon properties of strained two-dimensional materials while using attention mechanisms to reveal shear strain as the critical interaction center, thereby overcoming the computational limitations of traditional methods for deep elastic strain engineering.
This study demonstrates that intrinsic cation disorder in nominally cubic CuInSnS single crystals induces local symmetry breaking that decouples excitonic and phononic behaviors, causing excitons to exhibit pronounced polarization anisotropy while phonons remain largely homogeneous, thereby enabling new anisotropic optical functionalities in cubic multinary semiconductors.
This paper introduces a new paradigm in the DL_POLY 5 molecular dynamics code that enables efficient simulation of very large systems (billions of atoms) by calculating key system properties on-the-fly, thereby eliminating the traditional bottleneck of storing and post-processing massive trajectory files.
This paper introduces a novel graph-theoretic framework that encodes the geometry and topology of space-filling polyhedra into dual periodic graphs to systematically generate crystal structures, offering a more efficient and interpretable alternative to conventional random generation methods for accelerating materials discovery.