A neural network protocol for electronic excitations of N -methylacetamide

Authors: Sheng Ye, Wei Hu, Xin Li, Jinxiao Zhang, Kai Zhong, Guozhen Zhang, Yi Luo, Shaul Mukamel, Jun Jiang - Proceedings of the National Academy of Sciences, PNAS 2019 cited by 71

-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.