2756 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 review summarizes recent advancements in time-resolved X-ray techniques, such as those utilizing XFELs and tabletop HHG sources, which enable element- and momentum-specific probing of ultrafast charge, spin, orbital, and lattice dynamics in transition-metal compounds.
This study demonstrates that epitaxial (110) growth of ferroelectric perovskites, unlike the conventional (100) orientation, stabilizes diverse metastable nanoscale states and complex domain structures under modest strain, offering enhanced potential for functional tunability and large reversible responses.
This paper presents a "waterless" conductive atomic force microscope lithography technique compatible with vacuum and cryogenic environments that enables the creation of nonvolatile, reconfigurable oxide nanoelectronics with 0.85 nm resolution at millikelvin temperatures by engineering oxygen vacancies to control interfacial polaron-electron liquid transitions.
This study demonstrates that the charge density wave in quasi-one-dimensional ZrTe arises from a cooperative mechanism where momentum-dependent electron-phonon coupling, rather than Fermi surface geometry alone, plays the dominant role in driving the instability, provided that Hubbard interactions on Te orbitals are included to correctly reproduce the electronic structure.
This study identifies the three-dimensional spin-trimer magnet KBaCaCuVO as a promising candidate for a bipartite quantum spin liquid, demonstrating that weak microscopic exchange anisotropy is strongly amplified at the trimer level to stabilize a gapless, entangled ground state down to 20 mK.
This study utilizes focused ion beam-engineered targeted cleavage of RuO to acquire high-quality ARPES data, revealing that the material's electronic spectra are dominated by surface states exhibiting Rashba-type spin splittings due to spin-orbit coupling, which can be successfully disentangled from bulk contributions through comparison with density-functional theory.
This paper presents an autonomous scanning probe microscopy framework that integrates symbolic regression with large language models to generate and evaluate new physical hypotheses from sparse experimental data, successfully discovering interpretable voltage-time growth laws for ferroelectric domain switching without pre-specified models.
This paper develops a unified nonadiabatic theory for phonon magnetic moments in both insulators and metals using a gauge-covariant Wigner expansion, which successfully explains the experimentally observed large magnetic moments in PbSnTe by revealing significant contributions from Fermi-surface processes and resonant interband transitions beyond the adiabatic limit.
This paper introduces a physics-informed deep learning framework that compresses high-dimensional electronic charge density data into a compact latent representation, enabling the rapid and accurate prediction of key mechanical and thermodynamic properties for thousands of inorganic compounds using only a fraction of the computational resources required by traditional DFT calculations.
The paper introduces PFNet, a physics-informed neural operator framework that efficiently and accurately predicts microstructure evolution across diverse parameters and material systems by learning conditional evolution operators rather than direct correlations.