3396 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 study reports the experimental observation of higher odd-order (third-, fifth-, and seventh-order) nonlinear Hall effects in magnetic topological insulator Mn(Bi1-xSbx)2Te4 thin flakes, attributing the phenomenon to Berry curvature multipoles and demonstrating its dependence on the Néel temperature and charge neutral point.
This paper presents a systematic analytic inverse-design framework for temporal metamaterials that leverages space-time duality to directly synthesize closed-form refractive-index modulations for tailored wave responses, such as mathematical operators and specific filters, without requiring iterative optimization.
This paper introduces a symmetry-based framework called "order-(dis)order family trees" to evaluate the true novelty of predicted crystal structures by identifying their relationships to known disordered parent phases, revealing that many seemingly novel ordered compounds are actually derived from existing disordered materials and highlighting the importance of this distinction for improving data-driven materials discovery.
The paper introduces FINDSPINGROUP, an online computational platform that unifies spin space group and magnetic space group frameworks to automate symmetry analysis, classify magnetic phases, and facilitate the high-throughput discovery of unconventional magnets for next-generation spintronics.
GEWUM is a unified, open-source platform that automates materials discovery by integrating Selective Random Structure Search with universal Machine Learning Interatomic Potentials to enable efficient, scalable structure generation, selection, and validation on HPC clusters.
This paper presents the monolithic back-end-of-the-line integration of compact, energy-efficient VO-based spiking neurons on CMOS-compatible platforms, demonstrating gate-tunable oscillations, low-power operation, and tunable coupling that pave the way for dense neuromorphic hardware.
This study predicts the novel 1:1 kagome metal VSn as an intrinsic charge density wave material that, under pressure or doping, exhibits a rare anti-dome-shaped superconductivity intertwined with topological states and a reentrant CDW phase, offering a promising platform for exploring multi-phase quantum correlations and designing topological superconducting metals.
This study develops and validates a specialized machine learning interatomic potential (QCOF) based on the MACE architecture to efficiently simulate the thermal and mechanical properties of defective covalent organic frameworks, revealing distinct defect sensitivities in CTF-1 and COF-LZU1 systems while establishing a robust framework for large-scale quantum-accurate modeling of extended network materials.
This paper presents a design optimization scheme for magnetic tunnel junction-based sensors that balances flux concentrator gain and magnetic noise through finite element simulations and analytical modeling, ultimately achieving a three-orders-of-magnitude performance improvement over single-junction designs.