2841 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 demonstrates the fabrication of amorphous molybdenum silicide (MoSi) superconducting shells on individual Ga2O3 nanowires via magnetron co-sputtering, achieving an optimized critical temperature of 7.25 K to enable scalable quantum device applications.
This paper introduces "Ara," an LLM-based agentic workflow that leverages chemical priors to efficiently navigate the design space of covalent organic frameworks, successfully identifying durable and active photocatalysts for solar hydrogen production with significantly higher hit rates and faster convergence than random search or Bayesian optimization.
This study demonstrates the epitaxial growth of quasi-free-standing rhombohedral 3R-WSe2 bilayers on a cubic W(110) substrate via selenium passivation, confirming their intrinsic ferroelectric stacking and unique electronic properties through combined experimental and theoretical analysis.
This paper introduces a multi-fidelity machine learning framework incorporating global defect charge embeddings to accurately model charged point defects in Sb2Se3, enabling the prediction of stable structures and charge-transition levels at a fraction of the computational cost of direct quantum mechanical calculations.
This paper demonstrates that focused ion beam irradiation of metastable FeNi thin films induces a localized fcc-to-bcc phase transformation, enabling the precise patterning of ferromagnetic nanostructures with controllable crystallographic orientations and magnetic easy axes driven by residual lattice strain.
This study combines first-principles calculations and Raman spectroscopy to investigate the lattice dynamics of TaTe, NbTe, and their solid solutions, revealing that a specific high-frequency vibrational mode dominated by transition-metal motion exhibits unique intensity redistribution rather than frequency shifts, suggesting its short-range character and relevance to the charge density wave-driven lattice distortion.
This theoretical study demonstrates that pulse-duration-sensitive high harmonic generation in the chiral Weyl semimetal RhSi not only extends to higher photon energies by promoting multi-band excitations but also enables the synthesis of attosecond locally chiral light with pronounced circular dichroism, offering a pathway toward compact chiral light sources and topological electronics.
This paper presents a shift-invariant deep learning framework using Spatial Transformer Networks that effectively corrects for spectral shifts in X-ray Photoelectron Spectroscopy data, achieving high accuracy in identifying chemical functional groups and advancing automated material analysis.
This paper presents an automated computational workflow that integrates adaptive annealing to efficiently screen large polymer libraries, thereby generating consistent data suitable for machine learning models to predict key properties like density and glass transition temperature.
This paper addresses the challenges of modeling Y6 non-fullerene acceptors by tuning a range-separated hybrid functional to account for solvatochromic effects and oscillator strength borrowing in solid-state dimers, while cautioning that standard range-separated hybrids may be less accurate than global hybrids and proposing that reducing the range-separation length can improve accuracy without complex tuning.