4173 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 utilizes density-functional theory and Monte Carlo simulations to demonstrate that while grain boundaries in bcc iron induce local antiferromagnetic coupling and are modulated by phosphorus segregation, realistic boundary densities only minimally reduce the material's global Curie temperature due to the dominance of bulk-like regions.
By combining deep potential molecular dynamics with high-fidelity electronic-structure data, this study resolves decades of uncertainty to precisely locate aluminum's liquid-vapor critical point at approximately 6531–6576 K, 0.637 g/cm³, and 1.6 kbar, establishing a transferable framework for predicting critical phenomena in metals under extreme conditions.
ReadMOF introduces a novel, structure-free machine learning framework that leverages pretrained language models to convert systematic MOF nomenclature into semantic embeddings, enabling accurate property prediction and chemical reasoning without relying on atomic coordinates or connectivity graphs.
This study demonstrates that first-principles calculations reveal a Janus MoSSe/silk van der Waals heterostructure, whose strain-tunable interfacial electrostatics and enhanced polarization significantly amplify triboelectric charge density and output, establishing it as a promising candidate for high-performance nanogenerators.
This study utilizes first-principles density functional theory calculations to demonstrate that the Li|Li3OCl interface exhibits stable structural and electronic characteristics with unfavorable lithium insertion, suggesting that Li3OCl is a promising solid electrolyte for solid-state lithium batteries.
This paper introduces SevenNet-Nano, a lightweight universal machine-learning interatomic potential that leverages knowledge distillation from a large foundation model to achieve high accuracy and transferability while significantly reducing computational costs for large-scale atomistic simulations.
This study demonstrates that synthesizing Sn1-xInxTe via solid-state reaction and employing Rietveld refinement and Williamson-Hall analysis reveals that In doping substitutes Sn and introduces minor embedded phases, with the Sn0.96In0.04Te composition achieving the optimal balance of maximum host phase purity and highest thermoelectric power factor.
This paper presents the GPU acceleration of the Abinit code for plane-wave DFT calculations, detailing the algorithmic revisions and vendor library integrations required for multi-GPU scalability while comparing the performance of Locally Optimal Block Preconditioned Conjugate Gradient and Chebyshev polynomial filtering diagonalization methods on heterogeneous CPU-GPU architectures.
Through self-flux synthesis, researchers discovered a new family of ladder-like alkali chromium tellurides (CrTe, where = Rb, Cs) that feature a unique hybrid crystal framework and exhibit distinct antiferromagnetic and ferrimagnetic ground states, demonstrating the effectiveness of flux growth for designing complex low-dimensional magnetic materials.
This paper derives exact analytical expressions for pore-surface and surface-surface correlation functions within the general class of dead-leave models, demonstrating their validity for arbitrary grain shapes and dimensions while highlighting distinct structural differences compared to numerically reconstructed Debye random media.