2806 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 integrating analytic approximate Bayesian last-layer neural networks with uncertainty-driven active learning significantly accelerates photonic band gap prediction by reducing the required training data by up to 2.6 times compared to random sampling, thereby enabling more efficient surrogate modeling and inverse design for photonic crystals.
Neutron diffraction studies on single-crystal HoTe3 reveal two distinct antiferromagnetic phases with specific stripe motifs and stacking orders that are decoupled from the charge density wave order, suggesting that the alignment of propagation vectors and single-ion anisotropy are critical for spin-charge coupling in van der Waals systems.
This paper presents a multiscale Monte Carlo model demonstrating that ion sputtering yields from loose powders differ significantly from flat surfaces, being dominated by backward-directed ejecta at non-zero incident angles and exhibiting a universal energy dependence for normal incidence.
This study reveals that ferromagnetic-based magnetoreception is widespread across diverse bee species and non-bee outgroups without phylogenetic signal, while its strength correlates with increased body size and social behavior.
This study reveals that in a non-Hermitian Su-Schrieffer-Heeger chain, the competition between the skin effect and Aubry-André-Harper quasiperiodic disorder generates a unique reentrant delocalization regime and distinct five-phase landscape, while simultaneously destroying point-gap topology and suppressing entanglement entropy.
This paper proposes and theoretically validates a gate-tunable synthetic antiferromagnet based on a graphene/MnS/graphene heterostructure, where breaking structural symmetry induces dominant nonrelativistic spin splitting and giant magnetoresistance, offering a promising platform for efficient spintronic devices.
This study demonstrates that a first-order structural phase transition involving Ge-Ge dimerization is intrinsically coupled to and essential for stabilizing the long-range charge-density-wave order in the kagome metal FeGe.
This paper presents a unified machine learning framework that combines machine-learned interatomic potentials with neural classical density functional theory to enable efficient, first-principles multiscale modeling of liquid thermodynamics and phase behavior across homogeneous and inhomogeneous systems.
This study demonstrates that applying Laser-Enhanced Contact Optimization (LECO) to atmospheric copper fire-through metallization on phosphorus-doped p-PERC solar cells induces stable Cu3Si interfaces, significantly reducing series resistance and enabling a scalable, silver-free pathway for high-efficiency, cost-effective solar cells.
This study demonstrates that while belt speed during copper fire-through metallization initially affects the electrical performance of PERC solar cells, subsequent LECO treatment effectively neutralizes this impact, resulting in identical high-efficiency outcomes (20.8%) across different processing speeds.