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https://www.selleckchem.com/products/Fedratinib-SAR302503-TG101348.html To clarify the cis-trans isomerization mechanism of simple alkenes on the triplet excited state surface, the photochemistry of acyclic and cyclic vinyl ketones with a p-methoxyacetophenone moiety as a built-in triplet sensitizer (1 and 2, respectively) was compared. When irradiated, ketone 1 produces its cis-isomer, whereas ketone 2 does not yield any photoproducts. Laser flash photolysis of ketone 1 yields a transient spectrum with λmax ∼ 400 nm (τ ∼ 125 ns). This transient is assigned to the first triplet excited state (T1) of 1, which presumably decays to form a triplet biradical (1BR) that is shorter lived than the triplet ketone. In comparison, laser flash photolysis of 2 reveals two transients (τ ∼ 20 and 440 ns), which are assigned to T1 of 2 and triplet biradical 2BR, respectively. Density functional theory calculations support the characterization of the triplet excited states and the biradical intermediates formed upon irradiation of ketones 1 and 2 and allow a comparison of the physical properties of the biradical intermediates. As the biradical centers in 2BR are stabilized by conjugation, 2BR is more rigid than 1BR. Therefore, the longer lifetime of 2BR can be attributed to less-efficient intersystem crossing to the ground state.We present a machine learning (ML) method to accelerate the nuclear ensemble approach (NEA) for computing absorption cross sections. ML-NEA is used to calculate cross sections on vast ensembles of nuclear geometries to reduce the error due to insufficient statistical sampling. The electronic properties-excitation energies and oscillator strengths-are calculated with a reference electronic structure method only for a relatively few points in the ensemble. The KREG model (kernel-ridge-regression-based ML combined with the RE descriptor) as implemented in MLatom is used to predict these properties for the remaining tens of thousands of points in the ensemble wi

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