3396 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 identifies that thermal runaway in lithium-ion batteries is triggered by a combination of reduced local conductivity, heat capacity changes, and intercalation-induced heating, which create destructive thermal gradients and mechanical strain driven by phonon dynamics and grain architecture rather than traditional lithium rattler mechanics.
This study validates the feasibility of using an extended Stoner-Wohlfarth macrospin model to accurately describe the magnetic response of realistic, superellipsoid-shaped magnetite nanoparticles by directly comparing its predictions with full micromagnetic simulations across various geometries and sizes.
This paper introduces highly accurate and efficient Atomic Cluster Expansion (ACE) and MACE machine learning potentials trained on new DFT data, which significantly outperform existing models in predicting dislocation properties in Indium Phosphide (InP) while offering superior computational speed.
This study combines angle-resolved photoemission spectroscopy and density functional calculations to characterize the bulk and surface electronic structure of MoAlB(010), revealing distinct surface states with varying stability and spin-orbit splitting that are protected by mirror symmetry near the point.
This paper presents a computational framework based on density functional theory that successfully identifies specific dopants, such as Y, Co, and Ni, to promote the formation of radiation-tolerant amorphous grain boundary complexions in tungsten-rich alloys, a prediction validated by strong correlations with experimental sintering data.
This paper presents a predictive drift compensation method for scanning transmission electron microscopy (STEM) that analyzes past frames to dynamically adjust future scan grids at both pixel and frame levels, thereby mitigating sample drift and preserving image fidelity during multi-frame acquisition without relying on post-processing registration.
This paper presents an extended dual-solute segregation framework combined with machine learning to quantitatively predict co-segregation behavior in multicomponent alloys by accounting for solute-solute interactions, a method validated through simulations and experiments on magnesium-based systems to guide the design of optimized alloy properties.
This study demonstrates that machine learning models trained exclusively on high-quality experimental data, which explicitly incorporate indentation load alongside material descriptors, significantly outperform conventional DFT-based approaches and multi-task models in predicting load-dependent Vickers hardness.
This study resolves the long-standing puzzle of the Verwey transition in magnetite by presenting an ab initio-based model combining kinetic Monte Carlo and molecular dynamics to demonstrate that trimeron hopping, rather than significant band structure changes, governs polaron transport and accurately reproduces experimental dc-conductivity across the transition.
This study demonstrates that magnetoelastic effects significantly influence the magnetic anisotropy of [001]-textured Co80Ir20 thin films, where the choice of underlayer (Ta vs. Pt) induces varying degrees of strain that alter the effective anisotropy field, necessitating the consideration of stress effects when estimating magnetocrystalline contributions.