4797 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 combines experimental characterization and first-principles calculations to demonstrate that hexagonal boron nitride growth on a curved platinum substrate induces selective faceting into specific crystal planes by altering the relative stability of surface facets through varying overlayer-substrate interaction strengths.
This paper presents a graph-based inverse design framework that enables the architectural and modal programming of reconfigurable piezoelectric tensors, allowing a single structure to achieve programmable, frequency-selective sensing and actuation across multiple directions while significantly reducing volume compared to conventional assemblies.
This study demonstrates that chlorine substitution in Bi2O2Se effectively introduces van der Waals gaps to enhance its quasi-two-dimensional character and enable broad bandgap engineering while preserving key electronic properties like high electron mobility.
By integrating an AI-driven inverse-design workflow with machine-learning potentials and first-principles calculations, this study predicts two dynamically stable 2D Bi2O3 monolayers that exhibit exceptionally large out-of-plane polarizations, demonstrating that dimensionality-induced symmetry breaking is a viable strategy for discovering high-polarization 2D oxides.
This study demonstrates that training machine-learning force fields on less than 1% of the OMat24 dataset by selecting structurally and chemically distinct local environments yields accuracy comparable to models trained on the full 100-million-structure set, proving that chemical diversity rather than sheer data volume is the critical factor for general-purpose materials modeling.
By combining EPR and optical spectroscopy on Mn/Cr co-doped , this study reveals that an exchange-coupled Cr--Mn defect complex enhances emission through symmetry breaking rather than energy transfer, demonstrating that phosphor concentration optima are governed by defect speciation.
This study utilizes first-principles calculations to demonstrate that compressing orthorhombic Form III polytetrafluoroethylene induces an anisotropic thermal conductivity response where chain-axis conductivity consistently decreases while transverse components recover at higher pressures due to stiffer interchain forces and increased heat-capacity-weighted velocities.
This study utilizes in-situ high-resolution transmission electron microscopy and molecular dynamics simulations to reveal the temperature-dependent phase evolution, coarsening, and compositional heterogeneity of CoCrFeMnNi high-entropy alloy nanoparticles, demonstrating how atomic mobility drives enthalpic interplay at pre-melting temperatures.
This study demonstrates that in Mn-doped ZnGaO nanocrystals, the precursor stoichiometry is a critical design variable that governs dopant incorporation and defect chemistry, ultimately enabling Zn-excess compositions annealed at high temperatures to achieve superior X-ray luminescence compared to nominally stoichiometric samples.
This paper introduces SpectraFormer, a transformer-based deep learning model that accurately reconstructs and subtracts variable silicon carbide substrate signals from Raman spectra without reference measurements, thereby enabling the reliable detection and characterization of the previously inaccessible graphene buffer-layer (ZLG) signature.