A self-adapting first-principles exploration on the dissociation mechanism in sodiated aldohexose pyranoses assisted with neural network potentials

Understanding the mechanism of collision-induced dissociation (CID) in mono-saccharides with density functional theory (DFT) is challenging because of many possible reaction paths that originate from their high structural diversity. To search for the transition state (TS) from the huge number of conformers, we propose a three-step search scheme with the assistance of neural network potential (NNP). The search starts from a cross-checking of sugars, to a global search of all possible channels, and in the end, an exhaustive exploration around the low-lying channels. The cross-checking step quickly adapts the NNP from the studied molecules to the target ones. The other two steps utilize the adapted NNP to find the available pathways via random sampling of the structures. The study of the CID reactions in all eight types of aldohexose pyranoses was applied using the search scheme. The DFT calculations on AH-0 (Glc, Gal, and Man) in the previous study were utilized to construct an NNP and provide the TS structure database for searching AH-1 (All, Alt, Gul, Ido, and Tal). In total, we identified around 5200 TSs in AH-0 and AH-1, and the final NNP covers an energy range of more than 500 kJ mol −1 with a mean absolute error of energy less than 4 kJ mol −1 . The search scheme is useful not only for saccharides but also for highly flexible bio-molecules. A self-adapting first-principles exploration to understand the mechanism of collision-induced dissociation (CID) in mono-saccharides assisted with neural network potentials (NNP).