2692 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 that iron doping in GaSe crystals introduces optically and magnetically active defect centers, identified through power-, temperature-, and magnetic-field-dependent photoluminescence spectroscopy as Fe-bound excitons with distinct g-factors, thereby offering new insights for magneto-optoelectronic and quantum photonic applications.
This paper introduces a symmetry-electronic fingerprint (SEF) representation that, by integrating crystallographic symmetry and site-resolved electronic structure, enables machine learning models to accurately predict magnetic properties in 2D materials while uniquely utilizing model uncertainty as a diagnostic tool to identify and characterize competing magnetic phases and frustration.
This paper demonstrates that combining cepstral analysis with Green-Kubo simulations and machine-learned potentials provides a robust, automated, and efficient framework for accurately predicting the thermal conductivity of metal-organic frameworks by overcoming the statistical noise and parameter sensitivity inherent in conventional methods.
This paper demonstrates that topological circulators based on coupled quantum anomalous Hall insulators and resonators, modeled by an asymmetric non-Hermitian Hatano-Nelson system, achieve superior non-reciprocal performance with up to 50 dB isolation across a broad power range, offering a promising platform for integrating microwave devices with superconducting quantum information systems.
This study reveals that while magnetic skyrmion dislocations undergo a topological transition involving core splitting and extreme elongation driven by Dzyaloshinskii-Moriya interaction, their long-range strain fields surprisingly adhere to conventional Volterra elasticity theory, highlighting a fundamental distinction from polar skyrmion lattices where such elasticity breaks down.
This study combines machine learning-assisted multiscale simulations and continuum modeling to demonstrate that ionic interdiffusion at the LiCoO2|Li10GeP2S12 interface causes rapid capacity fade, while a LiNb0.5Ta0.5O3 interlayer effectively suppresses this diffusion but introduces a risk of delamination due to mechanical stiffness, highlighting the need for interlayers that balance low interdiffusion with low stiffness.
This study demonstrates that electric currents can efficiently control the chirality of helimagnetic domains in MnAu by inducing a transition from a multidomain state at significantly lower thresholds than direct chirality reversal, with the resulting chirality determined by the relative orientation of the current and magnetic field.
This study identifies that the underestimation of the Curie temperature in ferroelectric PbTiO primarily arises from limitations in exchange-correlation functionals rather than machine learning force field inaccuracies, revealing that apparent improvements from short-range models are fortuitous error cancellations while accurate predictions require explicit long-range interactions and improved functionals.
This paper introduces OMatG-IRL, a novel policy-gradient reinforcement learning framework that operates directly on velocity fields of continuous-time generative models to enable efficient, target-property-aligned crystal structure prediction without requiring explicit score computation.
This study resolves inconsistencies in experimental reports on RuO by demonstrating that while bulk and certain thin-film orientations are nonmagnetic, strain-induced altermagnetic spin splitting in specific (100) and (110) orientations generates a strong spin Hall effect without requiring Hubbard corrections.