4007 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 study demonstrates room-temperature spin injection and preservation in exciton-polaritons within a monolithic Tamm-plasmon microcavity embedding 2D halide perovskites, where rapid polariton decay outcompetes spin relaxation mechanisms to enable potential applications in chiral lasers and switches.
This study demonstrates that identifying single-photon emitters in hexagonal boron nitride using atomic-resolution ADF-STEM is fundamentally limited by sample thickness (beyond ~17 layers) and susceptible to misidentification due to residual threefold astigmatism, rendering the technique unreliable for the thicker flakes typically used in photonic research.
This study demonstrates that introducing Wadsley defects and transition metal disorder in MoNb12O33 anodes significantly enhances lithium diffusion kinetics and high-rate capacity by activating fast diffusion paths at lower lithiation levels, as confirmed through combined experimental characterization and machine-learning molecular dynamics simulations.
This study reveals that Molybdenum Carbide (MoC) nanosheets exhibit significantly longer carrier lifetimes than other transition metal carbides due to restricted phonon decay pathways caused by the large mass difference between Mo and C atoms, offering a promising strategy for enhancing photothermal and photovoltaic device performance through efficient hot carrier utilization.
This study utilizes a kinetic Monte Carlo model to demonstrate that while increasing chromium content in FeCrAl alloys suppresses vacancy diffusion—particularly in radiation-induced Cr-rich phases—higher iron content enhances vacancy mobility, potentially improving radiation tolerance by promoting defect annihilation.
This paper investigates how a non-local Chern-Simons topological term in Maxwell-Chern-Simons pseudo-electrodynamics generates a dynamical mass and energy gap for electromagnetic waves, inducing a novel hybridization between transverse electric and magnetic surface plasmon-polariton modes that is absent in conventional planar plasmonics.
This paper demonstrates that leverage-guided, label-free subset selection of ASSYST structure pools can significantly reduce the DFT labeling workload (by 2–3x) required to train accurate linear Atomic Cluster Expansion potentials while maintaining competitive energy, force, and defect-level fidelity compared to random and other baseline sampling strategies.
This paper argues for a renewed focus on accurate ionic diffusivity measurements in battery material development, highlighting that current practices often neglect proper length-scale assessment and that cell-level performance can diverge from intrinsic diffusivity values, necessitating rigorous best practices to establish meaningful structure-property relationships.
This paper presents GQD-AdsNet, a graph neural network framework trained on density functional theory data that rapidly and accurately predicts transition metal adsorption energies on graphene quantum dots, reducing computational costs by six orders of magnitude to enable efficient catalyst screening.
This study establishes a data-driven strategy for engineering tunable negative thermal expansion in metal-organic frameworks by utilizing a machine learning-accelerated workflow to screen over 12,000 structures, identifying key structural motifs for strong NTE that were subsequently validated experimentally with record-breaking performance in Ce-UiO-66 variants.