4962 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 reveals that hydrostatic pressure exerts opposite effects on the magnetic phases of NiBr2 compared to NiI2, where pressure suppresses the helimagnetic order while steeply enhancing the collinear antiferromagnetic order due to the dominant role of interlayer exchange interactions.
Neutron scattering experiments on the van der Waals antiferromagnet FePSe under uniaxial strain reveal that tensile strain induces a transition to symmetry in both magnetic order and spin excitations, providing direct evidence that the observed three-state Potts nematicity in the paramagnetic phase arises from vestigial order associated with the low-temperature zigzag antiferromagnetic state.
This paper introduces a modified reliability factor, , to replace Pendry's in quantitative low-energy electron diffraction, addressing its sensitivity to noise and intensity offsets while demonstrating superior or comparable performance in optimizing surface structure determination.
This study demonstrates that incorporating boron into 10 nm aluminum scandium nitride (AlBScN) thin films enables robust ferroelectric switching with significantly reduced leakage currents and coercive fields, establishing AlBScN as a promising candidate for low-voltage, CMOS-compatible nonvolatile memory applications.
This paper introduces a unified, differentiable probabilistic framework that simultaneously denoises atomic configurations, classifies crystal phases, and constructs order parameters by training on synthetic perturbations of known prototypes to robustly analyze complex atomistic simulations under diverse conditions.
This paper demonstrates that thermal fluctuations coupled with finite extensibility fundamentally alter the Euler buckling instability of semiflexible polymers, leading to a new critical regime where the critical compressional strain increases with system size and is governed by a distinct fixed point with unique critical exponents.
This paper proposes a Crystal Fractional Graph Neural Network that combines local atomic environment analysis via graph attention mechanisms with global compositional data to accurately predict the energy of high-entropy alloys, achieving first-principles-level precision on a dataset of over 1,000 structures while acknowledging current limitations with large crystal cells.
This study utilizes 3D numerical simulations to demonstrate that multiple, properly spaced fins significantly enhance the melting efficiency of phase change materials by leveraging interstitial spaces and avoiding the overlapping thermal effects that occur with sub-optimal single-fin or closely spaced configurations.
This paper demonstrates that the interface between epitaxially grown transition metal dichalcogenide (TMD) monolayers can be engineered to control the intensity and sign of Rashba spin splitting, thereby enabling highly efficient and tunable THz spintronic emitters through enhanced spin-to-charge conversion.
This paper introduces SLayerGen, a novel generative model that unifies the creation of both bulk crystals and diperiodic materials (such as 2D monolayers) by enforcing invariance to all space and layer groups through a hybrid architecture of autoregressive lattice sampling and equivariant diffusion, while also providing new datasets and metrics to advance the discovery of these previously underrepresented material systems.