4853 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 employs style translation to bridge the domain gap between simulated and experimental atomic force microscopy images, thereby enhancing the predictive performance of machine learning models for atomic structure discovery without requiring labeled experimental data.
This paper demonstrates that epitaxial strain applied to KTaO3 thin films grown on SrTiO3 substrates induces a robust, intrinsic ferroelectric state with a transition temperature of 475 K, enabling systematic control over the material's Curie temperature and spontaneous polarization.
This study reports the observation of spin-orbit-driven quarter semimetals in rhombohedral multilayer graphene, where strong correlations and proximitized spin-orbit coupling induce spontaneous symmetry breaking and a hysteretic anomalous Hall effect, ultimately enabling a magnetic-field-tuned phase transition to Chern insulators.
By combining elastocaloric experiments, free-energy modeling, and first-principles calculations, this study establishes a thermodynamic probe for the altermagnetic critical point in MnF, demonstrating how the coupling between magnetic multipoles and uniaxial strain can be used to detect multipolar order in -wave altermagnets.
The authors present a non-empirical, Kohn-Sham-assisted orbital-free density functional theory framework that achieves Kohn-Sham-level accuracy for electronic structure calculations under extreme conditions while offering computational speedups of several hundred times compared to traditional methods.
This paper identifies the family of -wave altermagnets as a versatile platform for realizing robust, spin-valley-locked nodal-line fermions protected by mirror symmetry, offering a general design principle for isolating these topological states through layer engineering and electronic correlations.
This perspective argues that the next frontier in energy materials discovery lies in developing thermodynamics-informed machine learning models that move beyond static zero-temperature energy predictions to capture the essential roles of entropy and anharmonicity in governing materials behavior under realistic operating conditions.
First-principles calculations reveal that ordered BiSbTeSe breaks inversion symmetry to exhibit giant bulk Rashba spin splitting alongside robust topological surface states, establishing it as a unique platform for investigating coexisting bulk and surface spin-charge transport phenomena.
This study presents physics-informed machine learning models that accurately predict cRPA-derived Hubbard interaction parameters (, , and ) for transition-metal oxides, offering both high-throughput screening capabilities and new physical insights into the electronic and structural factors governing these interactions.
PUDA introduces a headless, AI-native hardware harness for self-driving laboratories that replaces traditional graphical interfaces with a deterministic command-line runtime, enabling autonomous agents to execute experiments while ensuring full data provenance and auditability through structured, linked records.