2794 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 utilizes wide-field nitrogen-vacancy magnetometry to demonstrate that chiral molecular overlayers induce enantioselective and magnetic-field-dependent modifications to the size, spacing, and shape of skyrmions in CoFeB thin films, revealing a pathway for molecular control of topological spin textures via magneto-chiral coupling.
This paper proposes the structurally stable boron allotrope - as a pristine spinless topological semimetal that uniquely hosts a symmetry-protected two-dimensional nodal surface alongside multiple types of Weyl fermions, offering an ideal platform to study the interplay of multidimensional topological states.
This first-principles study demonstrates that twisting a -bismuthene monolayer by 30 onto a planar bismuthene layer on a SiC substrate induces unique orbital hybridization and Rashba spin-splitting, resulting in a robust and tunable Quantum Spin Hall phase with enhanced topological responses.
This study demonstrates that integrating natural language embeddings of synthesis and testing conditions with structural descriptors significantly enhances the accuracy and generalizability of machine learning models for predicting glass dissolution rates, thereby accelerating the discovery of durable nuclear waste immobilization materials.
This perspective outlines a generative design framework for inorganic materials that integrates foundation AI models, multi-modal learning, and high-throughput experimental validation to enable efficient data-driven inverse design for next-generation technologies.
This paper introduces "distributional inverse homogenization," a noninvasive methodology that leverages macroscopic mechanical property measurements to statistically infer global microstructural information, bridging probability and homogenization theory to solve a challenging class of inverse problems in material science.
This study demonstrates that incorporating high entropy alloys into bicontinuous nanoporous structures overcomes inherent macroscopic brittleness through strain hardening mechanisms like dislocation starvation and sluggish motion, resulting in materials with specific strengths 5 to 10 times higher than single-element counterparts and enhanced thermal resilience.
The paper introduces Diffusion-based Crystal Omni (DAO), a pretrain-finetune framework utilizing Siamese foundation models that significantly outperforms conventional methods in predicting crystal structures, achieving high accuracy on real-world superconductors while operating over 2,000 times faster than DFT-based approaches.
This study introduces a Fourier Neural Operator (FNO) based surrogate model that achieves resolution-invariant, rapid, and accurate prediction of multi-grain microstructural evolution, overcoming the computational limitations of traditional phase-field simulations and the generalization issues of existing machine learning approaches.
This study demonstrates that hafnia-zirconia superlattices with 87.5% ZrO content achieve record-breaking remnant polarization and endurance while promoting sustainability through the substitution of hafnium with abundant zirconium.