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  1. null (Ed.)
  2. UV absorption is widely used for characterizing proteins structures. The mapping of UV spectra to atomic structure of proteins relies on expensive theoretical simulations, circumventing the heavy computational cost which involves repeated quantum-mechanical simulations of excited-state properties of many fluctuating protein geometries, which has been a long-time challenge. Here we show that a neural network machine-learning technique can predict electronic absorption spectra of N -methylacetamide (NMA), which is a widely used model system for the peptide bond. Using ground-state geometric parameters and charge information as descriptors, we employed a neural network to predict transition energies, ground-state, and transition dipole moments of many molecular-dynamics conformations at different temperatures, in agreement with time-dependent density-functional theory calculations. The neural network simulations are nearly 3,000× faster than comparable quantum calculations. Machine learning should provide a cost-effective tool for simulating optical properties of proteins. 
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  3. The crosspropagation of 1‐ethylcyclopentyl methacrylate (ECPMA) and methyl methacrylate (MMA) has been studied using a combination of quantum chemistry calculations and experiment. Our computational work utilizes a trimer‐to‐tetramer reaction model, coupled with an ONIOM (B3LYP/6‐31G(2df,p): B3LYP/6‐31G(d)) method for geometry optimization and an M06‐2X/6‐311+G(2df,p) method plus SMD solvation model for single point energy calculations. The results show several trends: the identity of the ultimate unit of a trimer radical affects not only the preferred conformation of the region where the reaction takes place, but also the reactivity of the radical; the addition of an ECPMA monomer to the radicals is generally favored compared to an MMA monomer; the pen‐penultimate unit of a trimer radical shows a nonnegligible entropic effect; the penultimate unit effect is implicit for the ECPMA–MMA copolymer system. Finally, terminal model reactivity ratios fitted based on the explicit rate coefficients calculated from the quantum chemical results are compared with those from experimental measurements. The computations not only agree qualitatively with experimentally derived results in terms of the selectivity of ECPMA–MMA crosspropagation, but also give reasonable quantitative predictions of reactivity ratios.image

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