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Abstract Ultrafast optical switching in plasmonic platforms relies on the third‐order Kerr nonlinearity, which is tightly linked to the dynamics of hot carriers in nanostructured metals. Although extensively utilized, a fundamental understanding on the dependence of the switching dynamics upon optical resonances has often been overlooked. Here, all‐optical control of resonance bands in a hybrid photonic‐plasmonic crystal is employed as an empowering technique for probing the resonance‐dependent switching dynamics upon hot carrier formation. Differential optical transmission measurements reveal an enhanced switching performance near the anti‐crossing point arising from strong coupling between local and nonlocal resonance modes. Furthermore, entangled with hot‐carrier dynamics, the nonlinear correspondence between optical resonances and refractive index change results in tailorable dispersion of recovery speeds which can notably deviate from the characteristic lifetime of hot carriers. The comprehensive understanding provides new protocols for optically characterizing hot‐carrier dynamics and optimizing resonance‐based all‐optical switches for operations across the visible spectrum. 

Abstract Enantiomers are chiral isomers in which the isomer's structure itself and its mirror image cannot be superimposed on each other. Enantiomer selective sensing is critical as enantiomers exhibit distinct functionalities to their mirror image. Discriminating between enantiomers by optical methods has been widely used as these techniques provide nondestructive characterization, however, they are constrained by the intrinsically small chirality of the molecules. Here, a method to effectively discriminate chiral analytes in the nonlinear regime is demonstrated, which is facilitated by an upconverting chiral plasmonic metamaterial. The different handedness of the chiral molecules interacts with the chiral metamaterial platform, which leads to a change in the circular dichroism of the chiral metamaterial in the near‐infrared region. The contrast of the circular dichroism is identified by the upconverted signal in the visible region. 

Abstract Machine learning, as a study of algorithms that automate prediction and decision‐making based on complex data, has become one of the most effective tools in the study of artificial intelligence. In recent years, scientific communities have been gradually merging data‐driven approaches with research, enabling dramatic progress in revealing underlying mechanisms, predicting essential properties, and discovering unconventional phenomena. It is becoming an indispensable tool in the fields of, for instance, quantum physics, organic chemistry, and medical imaging. Very recently, machine learning has been adopted in the research of photonics and optics as an alternative approach to address the inverse design problem. In this report, the fast advances of machine‐learning‐enabled photonic design strategies in the past few years are summarized. In particular, deep learning methods, a subset of machine learning algorithms, dealing with intractable high degrees‐of‐freedom structure design are focused upon.