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.
This paper presents a fully unsupervised machine learning pipeline that integrates SOAP descriptors, autoencoders, UMAP, and HDBSCAN to automatically detect, classify, and characterize defect morphologies in displacement cascades across various materials without requiring manual template or threshold tuning.
This paper introduces a novel dropcasting method to isolate individual mineralized collagen fibrils for transmission electron microscopy, enabling the nanoscale visualization of their internal structure and the first-in-kind in situ tensile testing that reveals exceptional mechanical strains of at least 8%.
This paper introduces COFAP, a universal deep learning framework that leverages multi-modal feature extraction and cross-modal attention to achieve state-of-the-art, gas-agnostic prediction of covalent organic framework (COF) adsorption performance, thereby enabling efficient high-throughput screening and application-specific prioritization of candidate materials.
This study presents a systematic theoretical investigation of orbital torque in Ti/FM and Cu/FM bilayers, revealing that the torque's dependence on the ferromagnetic species varies with the nonmagnetic metal source and originates from the bulk nonmagnetic layer, thereby offering microscopic insights for designing light-metal-based orbitronic devices.
This paper presents a reinforcement learning agent utilizing geometric graph representations to efficiently optimize atomic ordering in bimetallic alloy nanoparticles, successfully identifying ground-state structures and demonstrating transferability across compositions and sizes, though with limitations in multi-element systems.
This paper proposes a hybrid architecture combining a CNN-based bijective autoencoder with a graph neural network to compress spatial dimensions and evolve grain growth in latent space, achieving significantly improved scalability, reduced computational costs, and enhanced long-term accuracy compared to GNN-only baselines for simulating realistic material microstructures.
Using crystal-structure prediction and ab initio calculations, this study identifies three new metallic ternary Fe-Si-O compounds stable at terapascal pressures that adopt pseudo-binary structures and suggest a fundamentally different mechanism for iron incorporation in super-Earth mantles, potentially triggering silicate dissociation at pressures below ~3 TPa.
This review examines the emerging paradigm of high-entropy perovskite oxides, highlighting how embedding extreme chemical disorder into the framework enables the discovery of novel electronic and magnetic phenomena through advances in synthesis, characterization, and theoretical understanding.
This study investigates many-body effects on spin-split electron bands in altermagnets by computing electron self-energies from magnon, phonon, and hybridized interactions, revealing that while thermal fluctuations and electron-phonon coupling broaden bands, the distinct spin-dependent broadening caused by electron-magnon coupling allows the intrinsic spin-splitting to remain spectroscopically resolvable.
This study demonstrates that hydrothermally grown ZnO/ZnS heterostructures on nickel foam significantly enhance energy storage performance through a predominant pseudocapacitive mechanism, where the ZnS component acts as a crucial hole reservoir to boost charge storage capabilities.