3992 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 demonstrates that combining Fourier Neural Operators with wavelet-based input encodings enables the efficient, high-fidelity prediction of multiple elastic wave eigenmodes in arbitrary metamaterial geometries, accelerating design cycles by three orders of magnitude compared to traditional finite element analysis.
This study employs first-principles calculations to reveal three key atomistic mechanisms—oxygen vacancy-induced diffusion acceleration, TiSr defect-promoted SrO island formation, and Ti insertion into SrO bilayers—that govern the molecular beam epitaxy growth of SrTiO3 and Sr2TiO4 thin films, offering critical insights for improving growth precision in metal oxide films.
This study demonstrates that resonant inelastic X-ray scattering (RIXS) can directly extract Quantum Fisher Information from spin fluctuation spectra, enabling the experimental detection of at least 7-partite spin entanglement in the cuprate SrCuO that persists at elevated temperatures.
Using density functional theory calculations up to 400 GPa, this study reveals that the ambient-pressure parabolic relationship between atomic volume and atomic number in transition metals evolves into a cubic-like behavior under extreme compression due to the higher compressibility of bcc structures.
This study proposes that above-bandgap photoexcitation can lower the energy barrier for ferroelectric switching in wurtzite crystals like LaN by inducing partial metallization that screens dipole-dipole interactions, offering a field-free, energy-efficient route for next-generation non-volatile memory.
EBSDmagus is a software tool designed to automate, manage, and ensure the reproducibility of large-scale, multi-stage dynamical electron backscatter diffraction simulations on high-performance computing systems by handling complex parameter variations, resuming interrupted jobs, and generating FAIR-compliant provenance records.
This paper introduces a squared-Hamiltonian framework demonstrating that lattice relaxation stabilizes the first magic angle in twisted bilayer graphene by balancing confinement and current-like channels near a special uniform confinement point, while simultaneously destabilizing higher-order magic angles through enhanced remote-band hybridization and localization.
This study reveals that perylene red microcrystals exhibit highly mobile singlet excitons driven by a J-like band and incoherent hopping transport, while demonstrating that the observed 610 cm⁻¹ spectral splitting arises from two distinct transitions rather than a traditional Davydov pair.
This paper introduces MLIP Detective, an agentic framework that uses physics-informed search to actively discover and characterize hidden failure modes in universal machine-learning interatomic potentials beyond standard benchmark evaluations, successfully identifying a systematic energy anomaly in the MACE-MPA-0 model.
This perspective article reviews two decades of research from the University of Nottingham that explores the full spectrum of tip-sample interactions in atomic force microscopy, emphasizing the probe's active role in phenomena ranging from van der Waals forces to covalent bonding and atom-by-atom assembly, while also addressing challenges in non-invasive diffusion measurements and the emerging application of machine learning in atomic manipulation.