2792 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 presents an autonomous large language model agent capable of end-to-end, data-driven materials theory development that successfully recovers established equations and proposes new relationships, while highlighting the critical need for careful validation due to the model's potential to generate mathematically fitting yet scientifically incorrect results.
This study reveals that structural disorder in the double perovskite GdSrCoMnO drives complex magnetic behaviors, including a Griffiths-like phase, spin-phonon coupling, slow magnetic dynamics, and a significant low-temperature exchange-bias effect, all stemming from the competition between ferromagnetic and antiferromagnetic interactions caused by the random distribution of mixed-valence Co and Mn ions.
This study presents a validated machine learning framework, utilizing an ensemble CatBoost model trained on 410 samples, to rapidly predict and optimize the mechanical properties of resorbable magnesium alloys by elucidating the critical roles of thermomechanical processing and specific alloying elements like Zn, Mn, and Gd.
This paper proposes a universal phenomenological law identifying a critical activation voltage (0.1–2.7 V) as the invariant threshold electrical work required to resonantly couple with the universal phonon damping peak, thereby unifying diverse field-driven phenomena ranging from flash sintering to electromigration across 17 crystal families.
This study demonstrates that rutile CuF exhibits giant chiral magnon splitting driven by a resonance-enhanced long-range super-superexchange mechanism, establishing it as a premier platform for spectroscopically validating altermagnetism in rutile structures.
This paper introduces a deep free energy learning framework that leverages the mathematical similarity between the self-consistent harmonic approximation free energy surface and potential energy surfaces to enable efficient, high-throughput crystal structure prediction incorporating finite-temperature and nuclear quantum effects, successfully identifying stable hydride structures in the La-Sc-H system with a million-fold reduction in computational cost compared to traditional methods.
This paper predicts and demonstrates an intrinsic, scattering-time-independent magnetoelectric Hall effect in layered nonmagnetic materials, which arises from a mixed layer-orbital quantum geometry and is bilinear in electric and magnetic fields, with rhombohedral pentalayer graphene serving as a key realization.
This paper experimentally demonstrates an acoustic quantum skyrmion-valley Hall effect in a phononic crystal, where engineered spin-orbit-momentum interactions generate robust, controllable skyrmion edge states that exhibit concurrent orbital angular momentum-valley and spin-texture locking.
This study employs machine-learning molecular dynamics to reveal that thermally driven interlayer sliding in ferroelectric MoS₂ moiré superlattices occurs via a domain-wall-mediated, ultralow-barrier collective reconstruction pathway rather than rigid translation, and that minimal sulfur vacancies can trigger a transition from long-range sliding to localized pinning.
This study demonstrates that large language models can effectively guide high-throughput experimentation to construct ternary phase diagrams, with a domain-specific LLM excelling at discovering complex interior phases and a general-purpose LLM efficiently identifying a broader range of phases through a textbook-like approach.