RESUMO
We have demonstrated that C60 derivatives bearing a pyrrolidine moiety as well as a variety of other substituents can form 1 : 2 complexes with γ-cyclodextrin (γ-CDx) using a mechanochemical high-speed vibration milling apparatus. When the influence of the steric hindrance of the substituents on the formation of the complexes was negligible, the water-solubilities of the complexes were shown experimentally to be completely dependent on the hydrophobic properties of the substituent. Furthermore, the stabilities of the γ-CDx-complexes of several different C60 derivatives were found to be similar to or slightly higher than that of the C60·Î³-CDx complex, with the solubilities of the complexes showing no correlation to the stabilities. Based on the results of a series of theoretical investigations, we have shown that the stabilities of the γ-CDx-complexes can be affected not only by steric effects but also by the polarities of the substituent groups, which exist in the vicinity of the upper rim of γ-CDx, because the water bound to the polar group can assist in the stabilisation of the complexes.
Assuntos
Fulerenos/química , gama-Ciclodextrinas/química , Modelos Moleculares , Estrutura Molecular , Solubilidade , Água/químicaRESUMO
Quantum machine learning for predicting the physical properties of polymer materials based on the molecular descriptors of monomers was investigated. Under the stochastic variation of the expected predicted values obtained from quantum circuits due to finite sampling, the methods proposed in previous works did not make sufficient progress in optimizing the parameters. To enable parameter optimization despite the presence of stochastic variations in the expected values, quantum circuits that improve prediction accuracy without increasing the number of parameters and parameter optimization methods that are robust to stochastic variations in the expected predicted values, were investigated. The multi-scale entanglement renormalization ansatz circuit improved the prediction accuracy without increasing the number of parameters. The stochastic gradient descent method using the parameter-shift rule for gradient calculation was shown to be robust to sampling variability in the expected value. Finally, the quantum machine learning model was trained on an actual ion-trap quantum computer. At each optimization step, the coefficient of determination [Formula: see text] improved equally on the actual machine and simulator, indicating that our findings enable the training of quantum circuits on the actual quantum computer to the same extent as on the simulator.