3525 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 review outlines the theoretical foundations and experimental signatures of excitonic insulators, offering strategies to distinguish them from competing phases while surveying candidate materials and identifying future research opportunities in the field.
This paper demonstrates that topological insulator-based single-electron transistors function as effective, magnetic-field-compatible charge sensors capable of detecting proximity charges and Zeeman-shifted trap states, thereby establishing a critical foundation for their future integration into hybrid devices for Majorana zero mode detection and braiding.
This study introduces the embedded local cluster expansion (eLCE) method to bridge first-principles calculations with kinetic Monte Carlo simulations, enabling the prediction of diffusion coefficients in multi-principal element alloys and revealing that local kinetic barriers, rather than thermodynamics, primarily govern whether diffusion is sluggish or anti-sluggish.
This paper introduces a material-agnostic platform using high-overtone bulk acoustic wave resonators (HBARs) to directly characterize spin-phonon interactions in complex crystalline materials at millikelvin temperatures, enabling the study of coupling strength and anisotropy with minimal fabrication constraints to advance hybrid quantum systems.
This study demonstrates that Mn substitution in trigonal induces a transition from a ferrimagnetic to a ferromagnetic ground state, significantly enhancing both the magnetic ordering temperature and saturation moment, as confirmed by combined experimental synthesis and first-principles calculations.
This study utilizes advanced microscopy, spectroscopy, and theoretical calculations to characterize the atomic structure and chemistry of 1D and 2D lepidocrocite TiO2, revealing that light element impurities like carbon drive the anisotropic growth of the 1D nanostructures.
This study demonstrates that Ru doping in MnP induces a highly anisotropic lattice expansion that selectively attenuates ferromagnetic coupling to stabilize helical magnetic order, significantly elevating the ordering temperature to 215 K and establishing a universal scaling relationship between the -axis lattice parameter and magnetic transition temperatures.
Using a D-Wave Advantage2 quantum annealer to overcome the sign problem in frustrated transverse-field Ising models, this study reveals a universal quantum suppression of ferromagnetic stability that remains constant across quasi-1D geometries before stepping down at a crossover scale to a 2D limit, thereby validating a predictive crossover law and confirming direct ferromagnet-to-paramagnet transitions.
This paper establishes an automated, high-throughput spin-assisted layer-by-layer liquid-phase epitaxy (LbL-LPE) protocol for the reproducible fabrication of highly oriented mixed-linker MOF thin films, validated through comprehensive correlative characterization to enable orientation-dependent applications.
This paper presents a highly accurate and scalable Graph Neural Network potential for aluminum, developed through a sequential-refinement workflow, which enables million-atom simulations to reveal how stacking-fault energetics and diffusion critically influence solidification microstructures and mechanical properties, outperforming both classical and general-purpose machine learning potentials.