2692 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 depositing a sub-1 nm silicon dioxide capping layer enables selective area molecular beam epitaxy of III-V semiconductors on highly reactive or non-selective mask materials like and , thereby overcoming the optical limitations of traditional masks without degrading their spectral performance.
This paper proposes a universal expression for the domain-wall width in multi-sublattice Heisenberg magnets by establishing an exact connection between the domain-wall profile and long-wavelength spin-wave dispersion, a framework that accurately predicts widths across various magnetic orders and lattice structures while providing a microscopic foundation for their temperature dependence.
The paper introduces DPA4, a novel SE(3)-equivariant interatomic potential architecture featuring an EMFA SO(2)-equivariant convolution and compiler-friendly training optimizations that achieve state-of-the-art accuracy with significantly reduced parameter counts and training costs, establishing a new accuracy-cost Pareto frontier for large atomistic models.
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 post-deposition annealing transforms RF magnetron sputtered films on Si(100) substrates from a mixture of nanocrystalline maghemite and amorphous strontium oxide into well-crystallized, c-axis textured SrFeO films, as confirmed by structural, compositional, and magnetic analyses including Mössbauer spectroscopy and X-ray absorption techniques.
This paper demonstrates that NbN-insulator-ferromagnet tunnel junctions, fabricated using a simple two-step process with an oxidized barrier, effectively enable reliable Meservey-Tedrow spin-polarization measurements of ferromagnets at 4He temperatures by exhibiting clear Zeeman splitting in the superconducting density of states for films thinner than 10 nm.
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.