4962 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 an automated deep learning framework utilizing an EfficientNetV1B0 Convolutional Neural Network to accurately classify nine distinct magnetic states, including complex antiferromagnetic textures, from RGB visualizations generated by atomistic spin dynamics simulations.
This paper introduces "Virp," a neural network-accelerated pipeline that combines permutation-based virtual cell generation, sampling, and thermodynamic post-processing to efficiently predict physical properties in site-disordered materials, overcoming the computational limitations of traditional methods by demonstrating that adequate configurational sampling can be achieved with just 400 virtual cells.
This study employs four simultaneous in situ techniques to systematically characterize Ga adsorption and desorption kinetics on GaN(0001) across various coverages, successfully validating a unified kinetic model and determining a Ga adlayer desorption activation energy of 2.87 ± 0.04 eV.
This study demonstrates that angle-resolved photoemission spectroscopy reveals an equilibrium-stabilized, hidden-phase-like metallic state in intermediate-thickness 1T-TaS2 flakes that persists up to room temperature while retaining characteristic hybridization gaps, offering a new platform for controlling competing electronic states in layered materials.
This paper reports the reproducible generation of an anomalous, spin-polarized linearly dispersing band with opposite helicity to the topological surface state in Bi-based topological insulator thin films, induced by soft Ar-ion bombardment and annealing.
This study employs first-principles calculations to demonstrate that hydrostatic pressure up to 40 GPa systematically tunes the electronic and optical properties of ferroelectric LaMoN, revealing an optimal regime near 15 GPa for enhanced photovoltaic efficiency through reduced exciton binding energy and maximized shift current density, thereby proposing a strategy for multi-junction solar devices.
This paper reports the first successful synthesis of Zr2SN2 thin films, a new class of metal sulfonitride transparent conductors that uniquely combine visible-light transparency, ultra-high refractive index (2.95), and degenerate n-type conductivity.
Using scanning tunneling microscopy and advanced theoretical modeling, this study reveals that a bulk hybrid transition-metal dichalcogenide (4Hb-TaS) hosts an emergent incommensurate potential arising from lattice mismatch between alternating 1T and 1H layers, which modulates interlayer charge transfer to drive the system toward a doped Mott regime and competes with bulk superconductivity.
This paper presents a multi-fidelity machine learning approach that fine-tunes a DFT-trained interatomic potential using limited quantum Monte Carlo energies to achieve near-QMC accuracy for simulating sulfur vacancy migration in monolayer MoS, enabling large-scale, high-precision simulations that are computationally prohibitive with direct QMC methods.
This paper proposes a topological cell-openness index () based on Betti numbers as a complementary or alternative metric to gas pycnometry for characterizing the open versus closed cell proportions in porous materials, while also demonstrating its correlation with physical quantities and its utility in estimating feature sizes.