The contradiction between the rising demands of optical chirality sensing and the failure in chiral detection of cryptochiral compounds encourages researchers to find new methods for chirality amplification. Inspired by planar chirality and the host–guest recognition of pillararenes, we establish a new concept for amplifying CD signals of cryptochiral molecules by pillararene host–guest complexation induced chirality amplification. The planar chirality of pillararenes is induced and stabilized in the presence of the chiral guest, which makes the cryptochiral molecule detectable by CD spectroscopy. Several chiral guests are selected in these experiments and the mechanism of chiral amplification is studied with a non‐rotatable pillararene derivative and density functional theory calculations. We believe this work affords deeper understanding of chirality and provides a new perspective for chiral sensing.

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A neural network model of tokamak discharge is developed based on the experimental dataset of a superconducting long-pulse tokamak (EAST) campaign 2016–2018. The purpose is to reproduce the response of diagnostic signals to actuator signals without introducing additional physical models. In the present work, the discharge curves of electron density n e, stored energy W mhd, and loop voltage V loop were reproduced from a series of actuator signals. For n e and W mhd, the average similarity between the modeling results and the experimental data achieve 89% and 97%, respectively. The promising results demonstrate that the data-driven methodology provides an alternative to the physical-driven methodology for tokamak discharge modeling. The method presented in the manuscript has the potential of being used for validating the tokamak’s experimental proposals, which could advance and optimize experimental planning and validation.