4173 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 machine learning model that extends the Gaussian Approximation Potential to include noncollinear magnetic degrees of freedom, enabling efficient ab initio spin dynamics with high accuracy by leveraging rotational symmetries and adiabatic approximations.
This study employs density functional theory to reveal that CaMnBi exhibits narrow-gap antiferromagnetism describable by a modified Heisenberg model, where small strain can tune the easy-plane magnetic anisotropy through the interplay of spin-orbit coupling and lattice distortions, highlighting its potential for spintronic applications.
This paper introduces vectorial field-guided lithography, a novel approach that utilizes fully structured polarization fields to quantitatively predict and programmably reconfigure pre-patterned azopolymer microstructures into complex anisotropic, bent, and chiral architectures through stress-driven mechanisms, thereby establishing a comprehensive theoretical framework for designing functional polymer surfaces beyond conventional intensity-based photopatterning.
This study demonstrates that magnetic field oscillations can effectively tune the non-flat energy landscape to control the formation and evolution of skyrmion lattice domains, thereby overcoming pinning effects to enhance quasi-long-range order in magnetic thin films.
This paper presents a resolution-dependent formulation for continuum dislocation dynamics that bridges the gap between fine and coarse scales by introducing a "line bundle" closure relation, which is demonstrated to be significantly more accurate than the standard maximum entropy closure for modeling orientation fluctuations in intermediate regimes.
This study demonstrates that fully dense (Cr,Mo,Ta,V,W)C high-entropy carbide ceramics, synthesized via carbothermal reduction and spark plasma sintering, exhibit tunable thermal and electrical properties—including thermal conductivity ranging from 7 to 12 W m⁻¹ K⁻¹ and a consistent Vickers hardness of ~29 GPa—that are significantly influenced by densification temperature and excess carbon content.
This paper extends a previously developed inverse problem framework for identifying viscoplastic constitutive behavior from full-field observations to dynamic indentation scenarios by incorporating contact constraints via Lagrange multipliers and slack variables, and validates the method using both synthetic data and experiments on rolled homogeneous armor steel and polycrystalline aluminum alloy.
This paper presents the synthesis of soft, cell-stiffness-matched elastomer-based whispering gallery mode microlasers that enable robust, tunable, and stable biosensing of biological forces through force-dependent laser mode splitting.
This study demonstrates that combining laser-enhanced contact optimization (LECO) with optimized firing temperatures effectively mitigates the high contact resistance and limited performance of cavitated fine-line Ag paste in PERC solar cells, thereby recovering fill factor and enabling practical low-silver metallization.
This paper presents an active learning workflow that integrates density functional theory, thermochemical modeling, and machine learning to screen over 70 billion candidates, resulting in a generalizable predictive model and the largest public database of CHNO explosives to date, which reveals oxygen balance as the primary driver of detonation performance.