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 spurious imaginary phonon modes, often arising from computational approximations in Metal-organic Frameworks, significantly distort heat capacity estimates and screening rankings, and proposes a rapid post-processing workflow to correct these errors and improve the reliability of thermal property predictions.
This paper introduces RADII, a comprehensive benchmark of ~75,000 crystal-derived nanoparticle structures that systematically characterizes the "extrapolation frontier" of graph generative models by revealing how their structural fidelity degrades as they generate materials beyond their training size, thereby establishing output scale as a critical evaluation axis for materials discovery.
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.
This paper reformulates the macroscopic Young equation on molecular grounds using a universal wetting coefficient derived from water's intrinsic hydrogen-bond energetics, thereby revealing wetting as an emergent property of water itself and establishing a predictive framework that unifies wetting, adhesion, and cavitation across diverse surfaces.
This paper uses numerical simulations to demonstrate that while discrete dislocation dynamics models with annihilation rules consistently converge to a PDE accounting for annihilation, models without collision rules exhibit inconsistent convergence behavior, highlighting the critical importance of carefully treating dislocation collisions in such simulations.
This paper introduces a novel symmetry-aware padding method that integrates Wyckoff position information into encoder architectures to significantly enhance the accuracy, stability, and efficiency of generative models for designing diverse inorganic materials, achieving notable improvements in reconstruction accuracy and the generation of novel stable compounds.
This study reports the discovery of easily saturated and anisotropic magnetostriction in the prototypical altermagnet MnTe, a finding that challenges traditional views on antiferromagnetic magnetostriction by revealing a symmetry-allowed coupling between elastic strain and the Néel order parameter.
This paper establishes a symmetry-constrained rule for light-polarization-robust magnetization in antiferromagnetic systems and demonstrates through theory and calculations that spin-spiral structures, unlike collinear antiferromagnets, enable ultrafast, polarization-independent spin manipulation via real-space demagnetization and rotation.
This study demonstrates that combining chlorine substitution and helium ion irradiation in the two-dimensional magnetic semiconductor CrSBr induces local symmetry breaking and defect-related scattering, which collectively reconstruct the anisotropic Raman spectra while preserving robust resonance-enhanced electron-phonon coupling.
This paper presents a self-evolving kinetic Monte Carlo framework that dynamically trains machine-learning models on-the-fly to efficiently predict atomic diffusion rates for thin-film growth simulations, achieving high computational efficiency and accuracy by replacing expensive calculations with uncertainty-guided learning, as demonstrated in Ag/Ag{111} sub-monolayer growth.