4940 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 introduces TEM Agent, a framework that leverages Large Language Models and the Model Context Protocol to enable text-based control of transmission electron microscopy subsystems, data management, and high-performance computing resources, thereby simplifying complex workflows without requiring additional model training.
This study demonstrates the quantum control of a strongly correlated Hubbard exciton in the one-dimensional Mott insulator SrCuO by using nonresonant midinfrared Floquet engineering to drive ultrafast rotations between bright and dark states, as quantified by resonant third-harmonic generation.
This paper develops a simple electrostatic two-equation model that accurately simulates dual-layer a-IGZO/a-IZO TFT operation by analyzing charge distribution and identifying an optimal top-layer thickness of 9 to 12 nm, while demonstrating the model's broader applicability to other dual-layer TFT systems.
This study uses ab initio calculations to demonstrate that framework materials, particularly the metal–organic compound Zn(NH)(formate), can generate significantly larger light-induced magnetic fields than traditional oxides by leveraging the circular motion of high-gyromagnetic-ratio hydrogen ions within their flexible structures.
This paper introduces a hybrid machine learning-accelerated solid-state nudged elastic band (SSNEB) framework that integrates EquiformerV2 and eSEN models with DFT to achieve up to a 7-fold speedup in calculating minimum energy pathways for solid-state materials while maintaining accuracy comparable to first-principles calculations.
First-principles calculations reveal that oxygen vacancies in multiferroic V-doped HfO donate electrons to V centers, reducing them to V and altering local magnetization and core-level shifts in a manner consistent with experimental XPS data, while suggesting that additional electron reservoirs are required to fully explain the valency ratios observed under ALD growth conditions.
This paper presents a custom-designed ultrahigh vacuum cluster tool that integrates in situ diamond surface preparation and characterization with cryogenic single nitrogen-vacancy center measurements to directly correlate surface chemistry with spin and charge properties for quantum sensing applications.
This study employs equivariant graph-based machine learning potentials, specifically the MACE architecture, to overcome computational limitations in simulating molten lithium carbonate, revealing that lithium transport is dominated by concerted motion and undergoes a temperature-driven transition from anisotropic to isotropic diffusion while accurately reproducing experimental structural and viscous properties.
This paper demonstrates that physical vapor deposition on aligned substrates can produce biaxially aligned organic glasses from both disk-like and rod-like mesogens at temperatures significantly below their clearing and glass transition points, thereby enabling new structural control for polarized emission and in-plane charge mobility in organic semiconductors.
This perspective paper outlines the necessity of integrating classical simulations, experimental measurements, machine learning, and quantum computing within reproducible, standardized workflows to overcome current limitations and accelerate the reliable discovery of advanced materials.