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Sungwoo Lee

Publications and source records attributed to Sungwoo Lee.

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Thermal decoupling in high-$T_c$ cuprate superconductors

In unconventional high-$T_c$ cuprate superconductors, the intricate interplay between the non-ergodic bad metal and the strange metal state has remained enigmatic. Herein, we unravel this mystery using ab initio molecular dynamics simulations and the temperature-dependent effective potential method. Our investigation, centered on YBa$_2$Cu$_3$O$_7$ , provides the first simulation report on the $B_{1g}$ phonon anomaly, unveiling thermal decoupling induced by the bond weakening between the Ba atom and CuO$_2$ plane. This decoupling emerges as a pivotal underpinning behind several puzzling phenomena in high-$T_c$ superconductivity. Our results indicate that the effective temperature on the BaO plane deviates from that of the CuO$_2$ plane at low temperatures. Furthermore, we delineate the correlation between thermal decoupling and the Planckian dissipation, rigorously and quantitatively revealing a connection between linear-$T$ resistivity, Uemura relation, and superconducting domes, which are known to be the most important unsolved mysteries of high-$T_c$ superconductivity. The suppressed isotope effect ($\boldsymbolα$ $\approx$ 0.02) in cuprates is also quantitatively explained from thermal decoupling. Our discoveries offer a revolutionary perspective on high-$T_c$ superconductivity, suggesting the potential for a transformative shift in our comprehension. Furthermore, they suggest that the autonomous emergence of low-temperature layers within materials has the potential to revolutionize industrial thermal management challenges.

cond-mat.supr-con

Life-inspired Interoceptive Artificial Intelligence for Autonomous and Adaptive Agents

Building autonomous -- i.e., choosing goals based on one's needs -- and adaptive -- i.e., surviving in ever-changing environments -- agents has been a holy grail of artificial intelligence (AI). A living organism is a prime example of such an agent, offering important lessons about adaptive autonomy. Here, we focus on interoception, a process of monitoring one's internal environment to keep it within certain bounds, which underwrites the survival of an organism. To develop AI with interoception, we need to factorize the state variables representing internal environments from external environments and adopt life-inspired mathematical properties of internal environment states. This paper offers a new perspective on how interoception can help build autonomous and adaptive agents by integrating the legacy of cybernetics with recent advances in theories of life, reinforcement learning, and neuroscience.

cs.AI