4797 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.
The paper proposes a fault-tolerant quantum computing architecture using multi-spin molecules that combines a hybrid encoding scheme to suppress pure dephasing errors and correct residual off-diagonal errors, thereby overcoming the coherence loss limitations of single-spin systems.
This study demonstrates that magnetic (Cr) and nonmagnetic (Cu) substitutions in the kagome antiferromagnet MnSn offer distinct pathways to engineer noncoplanar spin chirality and enhance the topological Hall effect through exchange enhancement and exchange dilution, respectively.
Using molecular dynamics simulations with machine learning potentials, this study reveals that 10 nm silicon nanoparticles undergo a stress triaxiality-driven, two-step Si-I to Si-V phase transition mediated by an intermediate amorphous shell, which subsequently exhibits a reversible amorphous-to-Si-V phase memory effect upon cyclic loading.
This paper introduces LATHE, a language-driven toolkit that enables fine-grained, differentiable editing of crystal structures based on seven geometric and symmetry constraints, facilitating a closed-loop multi-agent system that successfully translates natural-language hypotheses into targeted materials design.
The paper introduces datascribe_api, a Python library and CLI that provides researchers with a unified, typed, and FAIR-compliant interface to access and analyze both user-managed scientific data and curated materials databases via the DataScribe Cloud platform.
This paper introduces HyMLRaman, a hybrid framework that combines EfficientNet-B3 deep feature extraction with classical machine learning classifiers and DDPM-based generative augmentation to achieve high-accuracy identification of six pharmaceutical compounds from Raman spectra, even under limited-data conditions.
This study reveals that in ultra-thin CuCrP2S6, ferroelectricity and ionic transport exhibit a thickness-dependent antagonism where sub-40 nm flakes maintain robust ferroelectric switching while thicker samples transition to ionic-dominated memristive behavior, a dynamic further modulated by photoexcitation to enable multifunctional applications.
This study proposes the Janus MgAlB₂ MBene as a superior anode material for Li-ion batteries, demonstrating through first-principles calculations that its intrinsic out-of-plane polarization enables ultrafast Li-ion diffusion, a high theoretical capacity of 1470.24 mAh/g, and exceptional structural stability.
This study demonstrates that depositing the spin-1/2 nanographene [2]triangulene on a ferromagnetic TbAu2 surface alloy induces a proximity-driven splitting of its Kondo resonance, revealing a spatially modulated magnetic interaction that establishes TbAu2 as a viable platform for probing and integrating open-shell nanographenes with magnetic substrates.
This study systematically compares linear-response theory and a Hartree-Fock-based pseudohybrid functional approach for calculating Hubbard and parameters in transition-metal oxides, revealing that while both methods agree for partially occupied states, they yield significantly different results for nearly empty or filled shells and inter-site interactions due to their distinct underlying assumptions and sensitivity to pseudopotential choices.