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 employs first-principles calculations to map the structural, electronic, and hyperfine properties of the NaRTiO Ruddlesden-Popper series, revealing how ionic radius dictates the competition between ground-state symmetries and establishing Electric Field Gradient signatures as a sensitive probe for resolving these structures via experimental techniques like NMR and PAC.
This paper introduces \texttt{SingingMaterials}, a modular Python package that sonifies phonon density-of-states data from the Materials Project using three distinct approaches, and validates through a user study that auditory representations can effectively help listeners distinguish differences in material properties.
This paper demonstrates that heteroepitaxial -AlO grown on TiN via pulsed laser deposition forms a high-quality, single-crystal dielectric with an intrinsically low two-level system loss of , establishing it as a promising materials platform for reducing dielectric losses in superconducting quantum circuits.
This study utilizes non-empirical hybrid functional calculations to validate the electronic properties and surface stability of lead-free CsMX vacancy-ordered double perovskites, revealing that CsX-terminated surfaces avoid detrimental in-gap trap states and identifying specific candidates with optimal energy level alignment for next-generation optoelectronic applications.
This study demonstrates that while a long-range MACELES model significantly improves the quantitative accuracy of predicting transition temperatures, elastic constants, and dielectric constants for BaTiO3 compared to a local-only model, both approaches successfully reproduce the material's key qualitative ferroelectric behaviors such as phase transitions and polarization switching.
This study employs first-principles calculations to systematically investigate two-dimensional transition-metal trihalide monolayers, identifying MnF3 and PdF3 as promising candidates for the quantum anomalous Hall effect due to their spin-orbit coupling-induced band gaps, nonzero Chern numbers, and chiral edge states.
This study combines density functional theory with a recurrent neural network to demonstrate that biaxial bending-induced strain in wrinkled and nanobubbled MoS2 significantly outperforms uniaxial or in-plane strain in modifying electronic properties, offering a validated, computationally efficient framework for predicting local electronic structures in strained 2D semiconductors.
This paper humorously proposes that Mexican Burrowing Toads could serve as inexpensive gravitational wave detectors by amplifying cosmic signals through their nervous systems, though the authors found no evidence of such events in their recordings, ironically concluding that the lack of detection validates their unconventional approach.
This study combines density functional theory and region-resolved machine learning to demonstrate that the pi* region of XANES spectra is the most informative for predicting Bader charge and bond lengths, thereby establishing a robust method to quantify dopant-induced electronic modulation in boron- and nitrogen-doped graphene.
This paper presents a novel dynamic electron tomography framework that combines continuous tilting with self-supervised deep learning to enable continuous, dose-efficient 3D imaging of nanoscale structural transformations under operating conditions, overcoming the limitations of traditional static reconstruction methods.