4170 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 investigates the three-dimensional deformation and bifurcation behavior of inextensible ferromagnetic elastic rods under combined tension, twisting, and magnetic fields by formulating a reduced Hamiltonian system that reveals distinct post-buckling characteristics and localized modes differing between soft and hard ferromagnetic materials.
This paper proposes and experimentally demonstrates a non-Hermitian approach using an electric circuit platform to reshape high-order Landau modes via simultaneous magnetic, electric, and imaginary momentum fields, achieving multi-frequency single-peak localization for potential applications in information processing.
This paper resolves the contradiction between step bunching and step meandering in unstable step-flow growth by demonstrating that a (2+1)D Vicinal Cellular Automaton model and a continuum differential-difference PDE description can both capture their simultaneous coexistence and produce similar surface patterns when linked through a properly shaped potential energy landscape.
This paper presents a highly transferable machine learning interatomic potential for Ge-rich GeSbTe alloys, which was used to simulate nanosecond-scale crystallization at 600 K, revealing that kinetic effects during memory set operations lead to metastable phase separation into Sb-doped cubic GeTe and amorphous GeSb/Ge rather than thermodynamically stable products.
This study demonstrates that (101)-oriented RuO₂ thin films, a candidate altermagnet, exhibit giant room-temperature third-order electrical transport responses driven by simultaneous T-odd and T-even quantum geometric quantities, providing a robust method to detect Néel order and a versatile platform for quantum spintronic devices.
This paper presents a general analytical framework and open-source numerical tools to model light propagation and polariton behavior in planar heterostructures composed of an arbitrary number of rotated 3D anisotropic layers and 2D conductive sheets, thereby establishing a foundational theory for twist-engineered nano-optics.
This study utilizes wide-field nitrogen-vacancy magnetometry to demonstrate that chiral molecular overlayers induce enantioselective and magnetic-field-dependent modifications to the size, spacing, and shape of skyrmions in CoFeB thin films, revealing a pathway for molecular control of topological spin textures via magneto-chiral coupling.
This paper proposes the structurally stable boron allotrope - as a pristine spinless topological semimetal that uniquely hosts a symmetry-protected two-dimensional nodal surface alongside multiple types of Weyl fermions, offering an ideal platform to study the interplay of multidimensional topological states.
This first-principles study demonstrates that twisting a -bismuthene monolayer by 30 onto a planar bismuthene layer on a SiC substrate induces unique orbital hybridization and Rashba spin-splitting, resulting in a robust and tunable Quantum Spin Hall phase with enhanced topological responses.
This study demonstrates that integrating natural language embeddings of synthesis and testing conditions with structural descriptors significantly enhances the accuracy and generalizability of machine learning models for predicting glass dissolution rates, thereby accelerating the discovery of durable nuclear waste immobilization materials.