3501 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 the staggered dampinglike spin-orbit torque as the key mechanism for the deterministic electrical switching of the Néel vector in antiferromagnets with inversion+time reversal symmetry, demonstrating this effect through first-principles calculations and spin dynamics analysis in n-doped bilayer CrI.
This paper presents a theoretical framework and experimental validation demonstrating that atomic-level interfacial broadening in (SiGe)m/(Si)m superlattices induces localized energy states that create new optical absorption paths between 2 and 2.5 eV, enabling a non-destructive method to probe interfacial atomic structure.
This paper introduces a novel framework for time-dependent global sensitivity analysis applied to the Doyle-Fuller-Newman battery model, enabling the identification of insensitive parameters and the assessment of model error when those parameters are arbitrarily set, thereby facilitating more efficient simulative research on time-dependent outputs like voltage responses.
This paper demonstrates a scalable, high-yield monolithic integration of graphene Hall sensor arrays with silicon CMOS circuitry via vertical interconnects, overcoming previous routing limitations to enable dense magnetic sensing systems.
This paper introduces the Tensor Cluster Expansion (TCE) formalism, implemented in the open-source code tce-lib, which overcomes the limitations of standard cluster expansion methods on complex lattices by mapping correlation functions to mixed tensor contractions for efficient GPU-accelerated computation and near-constant-time energy difference calculations, while demonstrating high accuracy in modeling the TaW and CoNiCrFeMn alloy systems.
This study employs a multi-objective Bayesian optimization framework with an ensemble surrogate model and efficient sampling strategy to successfully identify Pareto-optimal high-entropy alloy compositions that simultaneously achieve high mechanical hardness and soft magnetic properties, overcoming the inherent trade-off between these characteristics.
This review systematically explores the multifaceted nature of altermagnetism in by analyzing its crystal and magnetic structures, electronic properties, and transport phenomena, while critically assessing the ongoing debate regarding its intrinsic magnetism and outlining future research directions.
This paper presents an efficient ensemble-based data assimilation framework that combines Smoothed Particle Hydrodynamics and the ensemble Kalman filter to automatically calibrate critical material model parameters for high-velocity impact simulations using data from a single test, demonstrating superior computational efficiency over traditional methods while providing diagnostic insights into parameter sensitivity and identifiability.
This paper introduces "continuous SUN" (cSUN), a unified and tunable metric that replaces heuristic binary thresholds with continuous formulations for stability, uniqueness, and novelty to provide granular evaluation of generative models for inorganic crystals and effectively guide reinforcement learning.
This study benchmarks five foundational machine learned interatomic potentials against DFT-NEB calculations to evaluate their accuracy in predicting ionic migration barriers, revealing that models like MACE-MP-0 and Orb-v3 excel in barrier prediction and high-throughput screening despite a lack of correlation with local geometry accuracy.