4940 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 presents a scalable, real-space first-principles method based on RS-LMTO-ASA and Chebyshev polynomial expansion to simulate orbital and spin transport phenomena in complex metallic multilayers, successfully demonstrating substantial orbital accumulation in centrosymmetric systems while naturally accounting for disorder and interface effects.
This study combines scanning tunneling microscopy and ab initio calculations to reveal that the Pt(111) surface catalyzes the thermal conversion of n-octane into benzene rings via intramolecular cyclization and into polycyclic species through a zipper-like intermolecular homocoupling mechanism at temperatures above 600 K.
This paper presents a comprehensive phase-field fracture model for brittle materials under thermal shock that independently specifies bulk properties, strength, and toughness, successfully unifying the prediction of diverse fracture scenarios—from crack propagation to nucleation and branching—across various experimental cases where classical approaches fall short.
By measuring the work functions of Li and Li metal nanoparticles, researchers discovered a significant isotope effect and non-trivial temperature dependence that challenge simple models and highlight lithium's nature as a quantum material where electronic and ionic degrees of freedom strongly interact.
This study demonstrates that topological antiferromagnet MnSn exhibits an unusually large near-infrared photoelastic effect driven by coherent acoustic phonons, revealing its exceptional sensitivity to lattice distortions and establishing a foundation for ultrafast straintronics applications.
This paper presents a computationally efficient, two-dimensional analytical model for characterizing toroidal magnetic rings with circular cross-sections under sinusoidal excitation, deriving explicit expressions for internal fields, impedance, and separated loss components to serve as an accurate alternative to finite element analysis for standardized material testing.
This paper demonstrates that equivariant graph neural networks, specifically an adapted GotenNet, significantly outperform existing state-of-the-art models in predicting optical spectra and static real permittivity for materials screening by leveraging geometric expressiveness and high-fidelity RPA-level training data.
AdsMind is a physics-grounded, closed-loop multi-agent framework that leverages machine-learning force field feedback to autonomously correct initial guesses and efficiently discover accurate adsorption configurations on heterogeneous catalyst surfaces, significantly outperforming open-loop methods and heuristic baselines in reliability and computational efficiency.
This study presents the first large-area observation of subsurface plastic activity in conditioned copper electrodes, demonstrating that high-field exposure induces significant intragrain misorientation consistent with evolving dislocation dynamics as the underlying physical mechanism for high-field conditioning.
This paper proposes a physics-informed framework that utilizes convex neural networks to discover anisotropic yield functions directly from full-field displacement and reaction force data, bypassing the need for direct stress observations or predefined parametric forms by embedding the yield function within a differentiable elastoplastic stress integration scheme.