3525 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.
AlphaDiffract is a deep learning framework utilizing a 1D ConvNeXt architecture trained on over 31 million simulated patterns to achieve state-of-the-art, single-shot prediction of crystal systems, space groups, and lattice parameters directly from experimental powder X-ray diffraction data.
This study demonstrates that combining magneto-optical imaging of magnetic flux distribution with quasiparticle spectroscopy via London penetration depth measurements provides an efficient method to correlate magnetic screening and in-gap states with internal quality factors, thereby enabling the optimization of epitaxial niobium films for superconducting qubits.
This perspective critically evaluates recent advances in ultrafast sintering, detailing its diverse heating mechanisms and applications while emphasizing the need for further mechanistic investigation to unlock its potential for high-throughput discovery of high-entropy and compositionally complex ceramics.
This study reveals that Mn2Sb2Te5 single crystals exhibit a field-induced ferromagnetic spin glass state and an anomalous Hall effect, establishing the Mn2(Bi/Sb)2Te5 system as a promising platform for exploring the interplay between intrinsic magnetism and topological band structures.
This paper presents a computational framework that combines parallel tempering molecular dynamics with machine learning to construct a finite-temperature structural chart for 38-atom AgCu nanoalloys, enabling the visualization of dominant structures across compositions and revealing distinct thermal stability differences compared to bulk alloys.
This paper demonstrates that by tuning ferroelectric phase boundaries to absolute zero via strain or chemical composition, quantum fluctuations can induce a saturation regime that yields a large, temperature-insensitive electro-optic effect at cryogenic temperatures, significantly outperforming existing materials like BaTiO3-on-Si.
This paper presents an active learning framework combining multi-objective Bayesian optimization with molecular dynamics simulations to efficiently discover and interpret polymer candidates that achieve an optimal trade-off between high thermal conductivity and low bulk modulus for advanced applications like flexible electronics.
This paper introduces MatSegNet, a boundary-aware deep learning model that enables high-throughput, accurate segmentation of carbide precipitates in high-strength steels, revealing that Lower Bainite and Tempered Martensite exhibit statistically similar carbide characteristics and challenging the conventional reliance on carbide orientation to differentiate these microstructures.
This paper introduces a generalized non-Abelian line graph theory that incorporates internal degrees of freedom and spin-orbit coupling to construct flat bands in multi-orbital systems, specifically demonstrating the realization of -orbital flat bands in Kagome lattices as a minimal model for transition metal materials.
This study proposes and experimentally demonstrates a general method to generate ubiquitous electronic flat bands in transition metal dichalcogenides through dilute intercalation, where interlayer geometric frustration induces destructive quantum interference that quenches kinetic energy and enhances many-body correlations.