4119 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 electric currents can efficiently control the chirality of helimagnetic domains in MnAu by inducing a transition from a multidomain state at significantly lower thresholds than direct chirality reversal, with the resulting chirality determined by the relative orientation of the current and magnetic field.
This study identifies that the underestimation of the Curie temperature in ferroelectric PbTiO primarily arises from limitations in exchange-correlation functionals rather than machine learning force field inaccuracies, revealing that apparent improvements from short-range models are fortuitous error cancellations while accurate predictions require explicit long-range interactions and improved functionals.
This paper introduces OMatG-IRL, a novel policy-gradient reinforcement learning framework that operates directly on velocity fields of continuous-time generative models to enable efficient, target-property-aligned crystal structure prediction without requiring explicit score computation.
This study resolves inconsistencies in experimental reports on RuO by demonstrating that while bulk and certain thin-film orientations are nonmagnetic, strain-induced altermagnetic spin splitting in specific (100) and (110) orientations generates a strong spin Hall effect without requiring Hubbard corrections.
APTLAS is a specialized, indexed repository and web tool containing approximately 2,300 Atom Probe Tomography publications with domain-specific metadata, designed to overcome the limitations of general search engines by enabling precise filtering and discovery of APT literature based on material systems, instruments, and analytical approaches.
This first-principles cRPA study reveals that the electronic screening and effective Coulomb interactions in carbon nanotubes are governed not only by metallicity but also sensitively by chirality and band topology, resulting in interaction strengths 2–3 eV lower than in nanoribbons and explaining the reduced exciton binding energies observed experimentally.
This paper introduces a range-aware Bayesian optimization framework that efficiently discovers diverse designs satisfying target property ranges by directly scoring the posterior probability of range compliance, demonstrating superior performance over standard methods in both benchmarks and practical materials design case studies.
This study demonstrates that monolayer CuF serves as a unique altermagnetic platform where symmetry simultaneously governs chirality-split magnons with quantized Chern numbers and cycloidal phonons, revealing a directional complementarity between spin and lattice chiral responses.
This study establishes that the speed of ultrafast demagnetization in Rare Earth-Transition-Metal ferrimagnets is governed by a universal orbital-mediated mechanism where the competition between 3d and 4f spin-orbit coupling channels determines whether angular momentum dissipates via rapid direct orbital-to-lattice transfer or slower multi-step pathways.
This paper demonstrates a scalable platform for integrated neural hardware by realizing programmable, cascading magnonic neurons in yttrium iron garnet waveguides that utilize nonlinear dynamics for self-normalized signal processing and phase-robust pattern recognition.