2772 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 trace doping of germanium anodes with large-atomic-size metals, particularly ytterbium, significantly enhances cycle stability by mechanically softening the material to suppress lithiation-induced cracking, thereby establishing mechanical compliance as a new design principle for high-capacity alloy electrodes.
This paper advocates for a paradigm shift from structure-centric to synthesis-first AI-driven materials discovery, proposing a roadmap that treats executable synthesis protocols as primary design variables to bridge the synthesizability gap through machine-readable representations, generative models, and closed-loop optimization.
This paper employs the Floquet-Magnus expansion to demonstrate that polarization ellipticity and the relative angle between electron momentum and the driving field serve as independent, tunable control parameters for the effective Rabi dynamics and occupation timing of resonantly driven Dirac electrons in graphene.
This theoretical study extends the framework for analyzing the Griffiths phase to three-dimensional antiferromagnetic and ferrimagnetic systems, revealing that their magnetic behavior is more unusual than that of conventional ferromagnetic systems while providing a method for identifying such phases.
This study reports a new cis-[Cd(Tz)₂(py)₂] complex that exhibits coordination-induced tuning of ligand-centered red emission, making it a promising warm-light semiconductor material for optoelectronic applications.
This paper introduces TrueEBSD, an open-source MATLAB add-on for MTEX that enables automatic image alignment and spatial distortion correction to facilitate correlative microscopy analysis by integrating EBSD maps with other imaging data for enhanced quantitative crystallographic measurements.
The authors developed accurate Density Functional Tight Binding (DFTB) models for cerium allotropes by globally optimizing electronic confining potentials, enabling the precise prediction of electronic band structures, energetic ordering, and complex f-electron interactions with minimal reliance on Density Functional Theory data.
By combining a unified atomistic framework with ultrahigh-field measurements on carbon nanotube fibers, this study reveals that macroscopic transport in disordered low-dimensional networks is primarily governed by junction-level quantum interference, where positive magnetoresistance stems from junction overlap and negative magnetoresistance arises from lattice-mismatched heterojunctions.
By introducing Zr to an Al-Gd near-eutectic alloy to form superlattice nano-layered fibres and core-shell nano-particles, researchers achieved a 400% increase in tensile ductility for cast aluminium alloys by preventing interfacial stress concentrations and promoting ultra-fine dislocation networks, thereby overcoming the catastrophic failure typical of brittle eutectic phases.
This paper introduces the xARPES Python code, which utilizes an extended maximum-entropy method with Bayesian inference to consistently extract electron self-energies and Eliashberg functions from curved dispersions in angle-resolved photoemission spectroscopy data, demonstrating superior accuracy on both model and experimental datasets compared to existing linearization-based approaches.