3499 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 theoretical study demonstrates that the antiferromagnet FeTe exhibits a large, temperature- and field-sensitive intrinsic anomalous Hall effect driven by Berry curvature, establishing it as a promising platform for exploring quantum transport in low-dimensional correlated systems.
This paper introduces a high-throughput AFM-based mechanical cleaning method using Pt-wedge modified cantilevers that significantly improves cleaning rates and sample quality for two-dimensional materials and their heterostructures, as demonstrated by enhanced photoluminescence in WS2 and better electrical contacts.
This paper establishes a unified, interpretable data-driven framework that decouples the governing mechanisms of hydrogen storage capacity and equilibrium pressure in interstitial hydrides, revealing that capacity is determined by geometric and thermal properties while pressure is controlled by elastic moduli, thereby enabling the rational design of materials with optimized performance.
This study employs HSE06 hybrid functional calculations to determine the properties of various copper-related defects in silicon, proposing a model to resolve discrepancies regarding the line and offering insights into copper precipitation mechanisms.
This paper presents an autonomous system that utilizes model-free visual reinforcement learning to align single crystals directly from Laue diffraction patterns without requiring prior crystallographic knowledge, thereby enabling intelligent, human-like experimental workflows in materials science.
This paper presents an energy-efficient artificial neuron based on annealing-optimized Ag/HfZrO memristors that exhibits multiple spiking functionalities, including leaky integrate-and-fire behavior and various coding modes, using a simple circuit without additional electronic overhead.
This theoretical study reveals that 2D ilmenenes exhibit intrinsic magnetic order with strong magnetocrystalline anisotropy and tunable spin orientations depending on the 3d metal composition, highlighting their potential for spintronic applications.
This paper demonstrates that while hybrid-TDDFT approaches improve upon standard long-range corrected kernels for calculating exciton binding energies, the persistent discrepancy between accurate spectra and binding energies stems from the numerical difficulties in treating the long-range Coulomb singular term, highlighting the need for a more robust description of electron-hole interactions.
This study utilizes picosecond acoustic strain pulses generated by femtosecond lasers to successfully probe the magnetic dimensional crossover in the van der Waals ferromagnet CrSiTe by detecting subtle spin-fluctuation-induced lattice responses and ultrafast carrier dynamics, thereby overcoming previous experimental limitations in observing this transition.
This paper introduces a machine learning model that extends the Gaussian Approximation Potential to include noncollinear magnetic degrees of freedom, enabling efficient ab initio spin dynamics with high accuracy by leveraging rotational symmetries and adiabatic approximations.