4181 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 isotropic strain can continuously induce and enhance scalar spin chiral order in the high-temperature collinear ferrimagnet Mn4N by selectively suppressing specific Mn-N orbital bonds, thereby transforming the magnetic ground state from collinear to noncoplanar without relying on external fields or chemical doping.
Using wide-field quantum microscopy, this study reveals that the superconducting transition temperature of optimally doped Bi-2223 single crystals remains robust up to 23 GPa in KBr but vanishes above 11 GPa in cBN, demonstrating the material's extreme sensitivity to the hydrostaticity of the pressure-transmitting medium.
This study utilizes a quartz tuning-fork resonator to quantitatively characterize the magnetic anisotropy of the layered ferromagnet CrGeTe, demonstrating its alignment with a quasi-two-dimensional easy-axis model and establishing the technique as a sensitive thermodynamic probe for distinguishing spin-origin anisotropy in low-dimensional magnets.
This paper presents a binary classification approach using random forest models trained on over 50,000 examples to predict the probability of glass formation in inorganic nonmetallic liquids with high accuracy (ROC-AUC ~0.89), while leveraging SHAP analysis to identify key features like bandgap energy and demonstrate that simplified models without stability parameters remain effective for composition screening and inverse design.
This study demonstrates that low-frequency noise serves as a sensitive probe for microscopic disorder in CVD-grown graphene, revealing that its significantly higher magnitude compared to exfoliated graphene stems from thermally activated dynamics of structural imperfections like grain boundaries and defects.
This study demonstrates that combining polar BiFeO and charge-transfer CaFeO in a superlattice induces cooperative lattice distortions that stabilize a non-centrosymmetric phase with polar charge ordering, C-type antiferromagnetism, and ferroelectric semiconductor behavior, establishing ferrite superlattices as a versatile platform for engineering multifunctional materials.
This study reports the successful growth of single crystals of the face-centered cubic antiferromagnet BaCoMoO and characterizes its structural, magnetic, and electronic properties, confirming a spin-orbit-entangled ground state with antiferromagnetic ordering at 20.1 K and highlighting its potential for spintronic and energy-conversion applications.
This paper presents a multi-agentic workflow that integrates AI agents with automated instruments to rapidly recover critical materials from complex real-world feedstocks via selective precipitation, reducing development timelines from years to days.
This paper proposes a novel quantum sensing technique using strain-tuned van der Waals heterostructures of Dirac materials (ZrTe5 and HfTe5) to selectively detect meV-scale impinging particles by filtering them based on their dispersion relations rather than just their energy, thereby overcoming the challenge of distinguishing signals from intrinsic excitations.
This study benchmarks various machine learning models for automating polarization mapping in ferroelectric potassium sodium niobate using 4D-STEM data, demonstrating that while synthetic training faces a simulation-to-experiment domain gap, specific training regimes and PCA-based methods can bridge this divide while also revealing that model errors correlate with crystal defects.