2822 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 demonstrates through 3D quantum wave packet calculations that bilayer graphdiyne membranes exhibit enhanced quantum transport and interlayer-separation-dependent resonances compared to monolayers, suggesting their superior potential for isotope separation applications.
This study employs a hierarchical approach combining density functional theory and custom-trained machine-learned interatomic potentials to perform high-throughput crystal structure prediction of zinc imidazolate, successfully identifying thousands of new energy minima and topologies while validating the method against experimental data and proposing promising candidates for future synthesis.
This study uses density functional theory to demonstrate that specific multi-component cubic halide perovskite alloys can simultaneously exhibit negative mixing enthalpy and rare upward band gap bowing due to s-s orbital repulsion, enabling the design of stable alloys with band gaps larger than any of their individual constituents.
This study establishes practical thin-film synthesis routes for -carbide type iron nitrides in Fe-W-N and Fe-Mo-N systems, revealing that Fe-Mo-N offers a broader stability window and exhibits tunable ferromagnetism dependent on stoichiometry, unlike the more compositionally restricted Fe-W-N.
This paper proposes a novel symmetry-driven generative framework that combines large language models for chemical semantics with a linear-complexity heuristic beam search to rigorously enforce algebraic consistency in Wyckoff patterns, thereby overcoming the combinatorial bottleneck in crystal structure prediction to achieve state-of-the-art performance in discovering new materials without relying on existing databases.
This study reveals that Moiré graphene domain-wall networks undergo hierarchical symmetry breaking driven by lattice relaxation, leading to the spontaneous emergence of distinct chiral network morphologies that fundamentally alter the localization and spectral characteristics of their topologically protected electronic states.
This review advocates for the adoption of microphysiological systems (MPS) as advanced, physiologically relevant models to overcome the limitations of traditional methods in assessing nanotoxicity, thereby enabling safer and more reliable risk-benefit evaluations for nano-enabled food innovations.
This study characterizes the 2017 Mukundpura CM2 carbonaceous chondrite from Rajasthan, India, using high-resolution microscopy and Raman spectroscopy to confirm the presence of 3–5 nm nanocrystalline diamonds alongside graphitic carbon and abundant iridium, offering insights into stellar evolution and the geological impact anomalies associated with mass extinctions.
This study demonstrates that Superhydrophobic Sand (SHS) mulch significantly reduces soil evaporation in arid regions by shifting the process from a temperature-controlled regime to a diffusion-limited one, with effectiveness governed by mulch thickness and soil thermal properties.
This study presents a high-throughput computational framework that successfully identifies seven bifunctional organic semiconductors, including an optimal candidate with 36.1% predicted power conversion efficiency and strong protein-binding affinity, by balancing photovoltaic performance against synthetic accessibility.