4119 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 warns that machine-learning interatomic potentials trained on PBE-based DFT data incorrectly predict the ground-state structure of HfO due to the functional's tendency to over-stabilize low-density phases, a flaw that can be mitigated by using alternative functionals like PBEsol or LDA.
This study reveals that introducing magnesium into lithium metal anodes induces a conditional spinodal decomposition between ordered B2 and Li-rich -BCC phases, creating a continuous interconnected microstructure that facilitates rapid lithium diffusion and suppresses dendrite formation at high current densities.
This study employs a novel approach combining photoluminescence imaging, drift-diffusion simulations, and Bayesian inference to map the spatially non-uniform degradation of perovskite solar cells, successfully distinguishing between bulk and interface defects and demonstrating that amino-silane passivation effectively suppresses interfacial degradation.
Using cryo-confocal microscopy and particle image velocimetry, this study reveals that volumetric expansion, rather than Marangoni flows, dominates fluid motion around bubbles during directional solidification, challenging existing theoretical models and offering new insights for controlling bubble distribution in solidified materials.
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.