457 research outputs found
Anomalous Hall magnetoresistance in a ferromagnet
The anomalous Hall effect, observed in conducting ferromagnets with broken
time-reversal symmetry, offers the possibility to couple spin and orbital
degrees of freedom of electrons in ferromagnets. In addition to charge, the
anomalous Hall effect also leads to spin accumulation at the surfaces
perpendicular to both the current and magnetization direction. Here we
experimentally demonstrate that the spin accumulation, subsequent spin
backflow, and spin-charge conversion can give rise to a different type of spin
current related magnetoresistance, dubbed here as the anomalous Hall
magnetoresistance, which has the same angular dependence as the recently
discovered spin Hall magnetoresistance. The anomalous Hall magnetoresistance is
observed in four types of samples: co-sputtered (Fe1-xMnx)0.6Pt0.4, Fe1-xMnx
and Pt multilayer, Fe1-xMnx with x = 0.17 to 0.65 and Fe, and analyzed using
the drift-diffusion model. Our results provide an alternative route to study
charge-spin conversion in ferromagnets and to exploit it for potential
spintronic applications
Smart Agent-Based Modeling: On the Use of Large Language Models in Computer Simulations
Computer simulations offer a robust toolset for exploring complex systems
across various disciplines. A particularly impactful approach within this realm
is Agent-Based Modeling (ABM), which harnesses the interactions of individual
agents to emulate intricate system dynamics. ABM's strength lies in its
bottom-up methodology, illuminating emergent phenomena by modeling the
behaviors of individual components of a system. Yet, ABM has its own set of
challenges, notably its struggle with modeling natural language instructions
and common sense in mathematical equations or rules. This paper seeks to
transcend these boundaries by integrating Large Language Models (LLMs) like GPT
into ABM. This amalgamation gives birth to a novel framework, Smart Agent-Based
Modeling (SABM). Building upon the concept of smart agents -- entities
characterized by their intelligence, adaptability, and computation ability --
we explore in the direction of utilizing LLM-powered agents to simulate
real-world scenarios with increased nuance and realism. In this comprehensive
exploration, we elucidate the state of the art of ABM, introduce SABM's
potential and methodology, and present three case studies (source codes
available at https://github.com/Roihn/SABM), demonstrating the SABM methodology
and validating its effectiveness in modeling real-world systems. Furthermore,
we cast a vision towards several aspects of the future of SABM, anticipating a
broader horizon for its applications. Through this endeavor, we aspire to
redefine the boundaries of computer simulations, enabling a more profound
understanding of complex systems.Comment: Source codes are available at https://github.com/Roihn/SAB
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