2898 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 a machine-learning-accelerated computational approach that uses surface-specific vibrational spectroscopy to demonstrate how graphene oxidation alters interfacial water structure, providing a quantitative spectroscopic fingerprint to reconcile conflicting experimental data.
This study demonstrates that applying an AC electric field can align 2D diopside flakes through flexoelectricity-induced acoustic strain, a process that enhances electrical conductivity and can be modeled through atomistic simulations for potential use in flexible electronics.
This study proposes an active learning approach to improve the prediction of enthalpy of mixing in binary liquids by identifying data gaps and performing targeted *ab initio* molecular dynamics simulations, specifically focusing on refractory elements.
This paper proposes the concept of "magnetic motifs"—specifically V-X-V triangular pathways—as a unifying principle to explain the magnetic ground states, transition temperatures, and ordering behaviors across a diverse range of fully and partially ordered VXAl Heusler alloys.
This paper reports the synthesis and characterization of single crystals, investigating their metallic electrical transport properties and electronic structure via ARPES and DFT to evaluate the material's potential as a realization of the topological checkerboard lattice.
HAMSTER is a physics-informed machine learning framework that predicts the quantum-mechanical Hamiltonians of large-scale, complex chemical systems by combining approximate physical models with minimal first-principles data, enabling accurate and scalable optoelectronic property prediction.
This paper demonstrates a proof-of-concept method for the contactless manipulation of interlayer twist angles in van der Waals heterostructures by using an electron beam to induce local electrostatic torque via charge injection into an insulating layer.
This paper provides a comprehensive review of the *exciting* all-electron software package, detailing its diverse range of high-level theoretical methods for studying electronic structures and various light-matter interactions, from density-functional theory to many-body perturbation theory.
This study demonstrates that inhomogeneous strain induced by creases in freestanding and membranes significantly suppresses local thermal conductivity by broadening phonon dispersion and enhancing scattering rates, offering a potential mechanism for developing active thermal management devices.
This paper utilizes atomistic simulations and machine learning to identify the key material properties—specifically elastic constants and lattice distortion—that govern local slip resistances in refractory multi-principal element alloys, ultimately developing a predictive model for macroscopic yield stress to guide alloy design.