4941 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 introduces Langevin Speculative Dynamics (LSD), a distributed and model-agnostic speculative sampling method that accelerates molecular dynamics simulations by 3–9x using a fast draft model and parallel verification without introducing relative error or compromising the accuracy of the target model's distribution.
This study demonstrates that ex-situ annealing transforms as-grown SrFeO films, which initially consist of nanocrystalline maghemite and amorphous strontium oxide, into well-crystallized, c-axis oriented strontium hexaferrite structures.
This paper demonstrates that NbN-insulator-ferromagnet tunnel junctions, fabricated using a simple two-step process with an oxidized barrier, can reliably measure the spin polarization of ferromagnets via the Meservey-Tedrow technique at temperatures up to 1.6 K.
Using cryogenic scanning transmission electron microscopy, researchers directly imaged the low-temperature structure of quantum paraelectric SrTiO3, revealing that its nanoscale polar domains initially self-organize into a periodic structure before fragmenting into small clusters as the material enters the quantum paraelectric regime below 40 K.
This paper benchmarks Tensor Train operations on CPUs, GPUs, and TPUs using JAX to adapt and accelerate a quantum-inspired SFFT-based homogenization algorithm, successfully enabling high-resolution multiscale simulations ranging from 300 million to 70 billion grid points that are infeasible with traditional GPU-based FFT methods.
By combining dispersion-corrected density functional theory with physics-informed machine learning, this study elucidates the sulfur adsorption and poisoning mechanisms across 30 transition metal 13-atom icosahedral clusters, identifying the Ti-Zr-Hf isoelectronic triad as a balanced group for designing sulfur-tolerant subnanometer catalysts.
The paper introduces ELECTRAFI, a fast and differentiable model that predicts periodic charge densities in crystalline materials by leveraging closed-form Fourier transforms of anisotropic Gaussians to achieve state-of-the-art accuracy with up to 633 times faster inference, thereby significantly reducing the total computational cost of DFT calculations.
This paper demonstrates that spurious imaginary phonon modes significantly distort heat capacity estimates and mislead machine learning benchmarks for Metal-organic Frameworks, and proposes a rapid post-processing workflow to correct these errors for accurate thermal property screening.
This paper introduces RADII, a radius-resolved benchmark that systematically characterizes the "extrapolation frontier" of graph generative models for materials science, revealing that structural fidelity degrades predictably beyond training radii and that this degradation pattern is unique to each model architecture.
This paper examines how the surge in AI and connected devices is driving a transition to edge computing architectures to meet unprecedented computational demands, outlining the key designs and metrics that will shape next-generation intelligent sensor systems.