2898 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 demonstrates that prestrained, hinged-beam circulators arranged in a hexagonal array can form a nonlinear mechanical metamaterial where snap-through bifurcations induce an effective time-reversal symmetry breaking, enabling robust, unidirectional propagation of elastic kink waves along interfaces without requiring magnetic or gyroscopic bias.
This paper presents an LLM-based pipeline that accurately extracts multicomponent alloy data from both text and tables to create the largest publicly available database of its kind, enabling sustainable materials design by identifying high-performance alloy candidates for lightweighting, soft magnetic, and corrosion-resistant applications.
This paper demonstrates a scalable, energy-efficient neuromorphic platform for image recognition and time series prediction using niobium oxide-based memristors with intrinsic random nanopores that function as a physical reservoir computing system.
This paper introduces DMFlow, a novel generative framework utilizing Riemannian flow matching and a specialized Graph Neural Network to effectively generate disordered materials (substitutional, positional, and mixed) while outperforming existing models designed solely for ordered crystals.
This paper introduces a scalar machine learning approach that successfully predicts Born effective charge tensors by leveraging scalar descriptors and the definition of polarization derivatives, offering an effective alternative to complex tensorial models for charge partitioning and finite-temperature infrared spectrum calculations.
This paper introduces an attention-guided training framework that integrates explainable AI with domain-specific physical priors to enhance the trustworthiness, generalization, and explanation fidelity of deep learning models for crack-tip segmentation in digital image correlation data.
Through a combination of single-crystal X-ray diffraction and spectroscopic techniques up to 63 GPa, this study provides conclusive experimental evidence for the re-emergence of a ferroelectric instability in the lead-free perovskite KNbO3, manifested as an incommensurate modulation involving cation displacements and oxygen octahedra tilts despite the centrosymmetric nature of the observed high-pressure phases.
This study reveals that ultrafast demagnetization and remagnetization-driven spin accumulation, governed by the reference-layer dynamics, are the key mechanisms enabling all-optical magnetization switching in multilayer spintronic devices.
This study demonstrates that incorporating the opposing effects of thermal expansion, which enhances electron-phonon coupling and overestimates resistivity, and phonon anharmonicity, which reduces it, provides a more accurate first-principles description of electrical resistivity in elemental metals like Pb, Nb, and Al.
This paper reports the discovery of alignment-dependent, gapless chiral split magnons confined within altermagnetic domain walls that operate in the microwave regime and can be manipulated via spin-orbit torque, offering a promising platform for novel magnonic nanocircuitry.