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 study demonstrates that dynamic dipolar coupling between a CoFeB artificial spin ice and an underlying NiFe film induces robust magnon-magnon interactions, resulting in a distinct triplet spectral feature and enabling the selective enhancement of specific spin-wave wavelengths for magnonic signal transport.
This paper demonstrates that Bayesian optimization can systematically identify optimal pulse parameters to achieve high-fidelity 6-bit temporal encoding in solution-processed In2O3/Al2O3 thin-film transistors for physical reservoir computing, while revealing that models trained on simpler tasks can effectively guide complex optimizations and that gate pulse amplitude and drain voltage are the most critical factors for state separability.
This paper presents a human-in-the-loop multi-objective Bayesian optimization framework that successfully identifies optimal photonic curing conditions for fabricating flexible aluminum oxide-based neuromorphic electronics, effectively navigating complex parameter spaces and high failure rates to balance large capacitance-frequency dispersion with low leakage current.
This study reveals that ReS2 exhibits unique layer-tolerant defect energy levels that remain invariant from monolayer to bulk due to the interplay of electronic minimization, structural relaxation, and weak interlayer coupling, establishing it as a robust platform for thickness-independent optoelectronic and quantum photonic applications.
This study re-analyzes experimental data using advanced signal processing techniques to confirm that high-purity ZnO nanocrystals can form a metastable planar hexagonal phase with lattice parameters significantly larger than previously reported, thereby resolving prior controversies and aligning experimental findings with first-principles computational predictions.
This paper demonstrates that field-free deterministic switching of perpendicular ferromagnets can be achieved in noncentrosymmetric Weyl semimetals, such as PrAlGe, by leveraging higher-order harmonics of spin-orbit torque to create new stable magnetization states, even in the presence of in-plane mirror symmetries.
This paper demonstrates that ferroelectric hafnia/zirconia resistive weights, fabricated with CMOS-compatible nanolaminates and scaled to under 100 m, enable energy-efficient 20 ns programming pulses (3 pJ) with an update rule where the final conductance is determined solely by the pulse amplitude, independent of the initial state.
This study uses density functional theory to demonstrate that normal stress significantly alters the stacking fault energies of six face-centered cubic metals, revealing that many classical and machine-learning interatomic potentials fail to accurately capture these stress-dependent trends.
This paper introduces an adaptive hybrid algorithm that integrates the Climbing Image Nudged Elastic Band (CI-NEB) method with Minimum Mode Following (MMF) to accelerate convergence to relevant transition states, demonstrating significant reductions in computational costs for high-throughput automated chemical discovery.
This paper introduces QUASAR, a universal autonomous system that leverages large language models to orchestrate complex, multi-scale atomistic simulation workflows and demonstrates its capability to function as a general reasoning agent for production-grade scientific discovery through rigorous benchmarking on tasks ranging from routine operations to frontier research challenges.