4797 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 "AI Theorist," an autonomous AI system that successfully generates and refines a first-principles physical model to explain optical spectra in -RuCl, marking the first instance of an AI independently discovering excitonic structures in a quantum material to bridge experimental observations with microscopic theory.
Classical reactive molecular dynamics simulations reveal that the preparation sequence of boron and lithium on tungsten surfaces, rather than the boron inventory, dictates oxygen partitioning and the resulting surface chemistry, with lithium acting as the dominant oxygen captor only in co-deposited substrates to form a lithium-borate-glass-like network.
This study demonstrates that controlled post-spray recrystallization in cold-sprayed 316L stainless steel serves as a powerful tool for characterizing deposition-induced deformation heterogeneity and tailoring microstructural and mechanical properties, driven by intense localized strain and high dislocation densities at particle boundaries.
This paper presents the first perturbation theory for one-dimensional quantum transport in disordered stealthy hyperuniform Kronig-Penney-like models, demonstrating that the unique combination of stealthy and hyperuniform constraints leads to the exact cancellation of lower-order scattering terms and a significant suppression of the Lyapunov exponent.
This paper presents a coercivity-aware machine learning framework that integrates experimental data screening with first-principles magnetic benchmarks to identify and prioritize rare-earth-free Fe-Co-rich soft magnetic alloys, while highlighting the critical importance of composition-grouped validation and the distinct influence of microstructure on coercivity prediction.
Using micro-focused angle-resolved photoemission spectroscopy, this study visualizes the tunable electronic structure of monolayer TaIrTe, revealing an electron-hole asymmetry in doping responses where hole doping induces a rigid band shift while electron doping causes band renormalization that shrinks the gap and alters band topology.
This paper resolves conflicting models regarding the deformation of hard magnetic soft materials by constructing counterexamples and establishing a first-principles framework that unifies these approaches into a single optimal closed-form law for rational device design.
This study demonstrates that site-selective interstitial doping of Pt in RhAs skutterudites significantly enhances and reverses spin Hall and spin Nernst conductivities by leveraging strong spin-orbit coupling and avoided crossings to overcome momentum-space cancellation, offering a promising route for robust spin-current generation.
This study utilizes Time Quantified Monte Carlo simulations to demonstrate that optimizing magnetic hyperthermia efficiency in anisotropic nanoparticle assemblies requires a self-consistent determination of local structural factors, specifically particle locations and easy axis distributions, derived from the equilibrium state of a dipolar hard sphere ferrofluid.
This paper presents a novel method for refining crystallographic orientations in nanobeam precession electron diffraction by analyzing internal spot intensity profiles, achieving significantly higher angular precision (0.044°–0.066°) and correcting systematic errors compared to standard automated crystal orientation mapping.