2756 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 reports that applying a magnetic field during the synthesis of MnBiTe single crystals stabilizes a metastable ferromagnetic ground state with a Curie temperature of ~12.5 K, effectively reconfiguring the material's spin order and electronic properties while retaining its original crystal structure.
This paper introduces a composition-weighted symbolic regression framework that combines hybrid search algorithms with max/min operators to generate interpretable, analytical expressions for predicting diverse materials properties directly from chemical composition, achieving competitive accuracy against black-box models while revealing chemically meaningful elemental trends.
This study reveals that the non-centrosymmetric AgI (110) surface hosts a robust persistent spin texture and sizable spin-orbital Hall conductivities, establishing halide semiconductors as a new platform for long-lived spin transport and tunable spin-orbit functionalities.
This paper demonstrates the reliable creation of conductive charged domain walls in insulating barium titanate through the combined action of light and electric fields, proposing a mechanism driven by the bulk photovoltaic effect and supported by phase-field simulations for potential use in reconfigurable opto-electronic devices.
This paper presents a unified theoretical framework for weakly disordered spin-1/2 ferromagnets that simultaneously describes the loss of saturation magnetization due to finite-size effects, derives a generalized Bloch equation, and provides a unified expression for spin transport.
This paper proposes and validates a strategy for constructing explicit functionals that directly map Kohn-Sham potentials to charge densities in inhomogeneous materials using homogeneous electron gas data, successfully demonstrating improved accuracy through increasingly sophisticated approximations without solving the Kohn-Sham Schrödinger equation.
This study characterizes the distorted honeycomb magnet as a frustrated high-spin antiferromagnet that exhibits short-range correlations, an unconventional field-induced transition, and exotic behavior near a mean-field tricritical point due to the interplay of competing exchange interactions and weak anisotropy.
Using molecular dynamics simulations with neural network potentials, this study reveals that rapid ion migration in MAPbI is driven by concerted collective motion of MA interstitials and charge-dependent I-related defects, while MA vacancies remain immobile, thereby revising the conventional understanding of ionic transport in hybrid perovskites.
This paper introduces a unified, single-descriptor framework derived from mean-field statistical mechanics that maps the complex, multi-site electrocatalytic activity of heterogeneous systems onto a single effective coordinate, thereby generalizing Sabatier-type analysis to capture the coupled effects of binding energetics and lateral interactions in arbitrary alloy catalysts.
This paper presents a novel AI-driven workflow that significantly expands the ALEXANDRIA database with 1.3 million DFT-validated compounds and 14 million out-of-equilibrium structures, achieving a 99% success rate in identifying stable materials while providing a comprehensive dataset for training universal force fields and advancing computational materials discovery.