2590 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.
PLaID++ is a preference-aligned language model that leverages a novel symmetry-informed Wyckoff text representation and temperature scaling to efficiently generate diverse, thermodynamically stable, and target-constrained inorganic crystal structures, outperforming prior methods by approximately 50%.
This paper demonstrates a scalable trans-scale spin Seebeck effect in nanostructured bulk composites of Pt-coated yttrium iron garnet, overcoming the diffusion-length limitations of conventional thin-film devices to enable isotropic, volumetric thermoelectric power generation.
This paper proposes that orbital altermagnetism can emerge in kagome metals like VSb through intertwined charge density-wave and loop-current instabilities, demonstrating that altermagnetic-like states are possible even in lattices with an odd number of sublattices when electronic interactions induce non-uniform magnetic moments.
This paper introduces photomodulated electron energy-loss spectroscopy (EELS) in an optically coupled scanning transmission electron microscope to directly image angstrom-scale photocarrier localization at oxygen-vacancy surface trap states in rhodium-doped strontium titanate nanoparticles, thereby elucidating nanoscale mechanisms that hinder solar water splitting.
This paper demonstrates that unsupervised nonlinear dimensionality reduction applied to the Inorganic Crystal Structure Database reveals a hidden low-dimensional geometric manifold that organizes crystalline materials, successfully segregating superconductors and enabling accurate prediction of critical temperatures without knowledge of the underlying pairing mechanism.
By combining fast differential scanning calorimetry with correlative STEM across a wide range of heating rates, this study reveals that interdiffusion in nanoscale Ni/Al multilayers proceeds hierarchically, transitioning from grain boundary-dominated transport at low temperatures to lattice diffusion at higher temperatures, thereby establishing grain boundaries as the primary control on reaction onset and microstructural design.
This paper demonstrates that compositional gradient engineering in ultrathin AlScN films mitigates leakage and breakdown by distributing structural discontinuities, thereby enabling robust ferroelectric switching in stacks as thin as 5 nm with significantly enhanced resistivity and polarization compared to homogeneous counterparts.
This paper evaluates seven fine-tuning strategies for machine-learned interatomic potential (MLIP) foundation models across diverse chemical benchmarks, revealing that while prerequisites like foundation model quality and correct energy initialization are paramount, naive fine-tuning is optimal for single-system accuracy whereas multihead replay uniquely preserves out-of-distribution robustness for broader deployment.
This paper proposes a unified framework linking electronic structure to intrinsic ductility by identifying shear amorphization as a lower-energy fracture criterion than dislocation nucleation, thereby enabling accurate predictions of ductility and ductile-to-brittle transitions for both pure metals and multi-principal-element alloys.
This paper warns that machine-learning interatomic potentials trained on PBE-based DFT data incorrectly predict the ground-state structure of HfO due to the functional's tendency to over-stabilize low-density phases, a flaw that can be mitigated by using alternative functionals like PBEsol or LDA.