4135 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 a multifidelity Mixture-of-Experts framework for machine learning interatomic potentials that spatially partitions simulation domains and employs a co-training strategy to resolve mechanical mismatches at interfaces, thereby achieving high-fidelity accuracy for complex catalytic systems at more than double the computational speed of standard methods.
This first-principles study reveals that the quasi-2D C2N2O material is a thermally stable, low-thermal-conductivity semiconductor with a tunable indirect band gap and strong anisotropic optical absorption, making it a promising candidate for nanoscale optoelectronic and thermal control applications.
This paper presents a high-yield, versatile strain platform that enables precise, reversible, and homogeneous uniaxial strain tuning up to ~5.5% in various two-dimensional materials, overcoming previous limitations in strain magnitude, repeatability, and cryogenic performance while also facilitating the study of strain gradients.
This study demonstrates that negative magnetoresistance observed in strained -Sn and -SnGe films under in-plane magnetic fields is inconsistent with the chiral anomaly hypothesis, suggesting alternative mechanisms are responsible for the effect.
This paper numerically investigates Turing patterns on non-fluctuating lattices using Finsler geometry to model mechanical stress, demonstrating that these static systems exhibit stress-induced pattern responses analogous to those observed on fluctuating membranes.
This study validates a transferable machine-learning potential, originally trained on solid-state properties, for accurately simulating the structural and dynamical characteristics of liquid Al-Ti alloys across various temperatures and compositions, revealing weak chemical ordering and strong agreement with experimental data.
This study utilizes DFT+U calculations to demonstrate that NO2 adsorption on -Fe2O3 quenches polaron-mediated conductivity by extracting electrons from the surface, thereby providing a microscopic explanation for the increased resistance observed in hematite-based gas sensors upon exposure to oxidizing gases.
This master's thesis utilizes a dual-methodological quantum chemical approach to reveal that ethylbenzene dehydrogenation on rutile TiO(110) proceeds via proton-coupled electron transfer on stoichiometric surfaces but shifts to a more efficient direct hydrogen atom transfer mechanism on oxidized surfaces, with photon energy determining whether the reaction bypasses ground-state kinetic barriers through excited-state persistence.
This paper presents a machine-learning framework that accurately predicts the electron localization function topology of dense hydrogen directly from atomic geometry, demonstrating high fidelity across fluid and crystalline phases while bypassing explicit electronic-structure calculations.
This paper presents a physics-based model that disentangles and analyzes the distinct degradation mechanisms of silicon-graphite composite anodes in lithium-ion batteries, specifically addressing the interplay between SEI growth, particle cracking, and active material loss under various cycling, storage, and check-up conditions to inform future battery optimization.