Polydopamine is the first adhesive polymer that can functionalize surfaces made of virtually all material chemistries. The material‐independent surface modification properties of polydopamine allow the functionalization of various types of medical and energy devices. However, the mechanism of dopamine polymerization has not yet been clearly demonstrated. Covalent oxidative polymerization via 5,6‐dihydroxyindole (DHI), which is similar to the mechanism for synthetic melanin synthesis, has been the clue. Here, it is reported that a physical, self‐assembled trimer of (dopamine)2/DHI exists in polydopamine, which has been known to be formed only by covalent polymerization. It is also found that the trimeric complex is tightly entrapped within polydopamine and barely escapes from the polydopamine complex. The result explains the previously reported in vitro and in vivo biocompatibility. The study reveals a different perspective of polydopamine formation, where it forms in part by the self‐assembly of dopamine and DHI, providing a new clue toward understanding the structures of catecholamines such as melanin.

Basin-hopping sampling has been widely used for searching local minima on a potential energy surface. Reaction intermediates including reactants and products are also local minima composed of a reaction path, but their brute-force sampling is too demanding because of large degrees of freedom. We developed an efficient Monte Carlo basin-hopping method to sample reaction intermediates through the fragmentation of molecules and a postanalysis scheme using the graph theory with a matrix representation of molecular structures. The former greatly reduces the dimension of a given potential energy surface, while the latter offers not only the effective screening of resulting local minima toward desirable intermediates but also their automatic ordering along a reaction path. We combined it with the density functional tight binding method for rapid calculations and tested its performance for organic reactions.

Machine learning based on big data has emerged as a powerful solution in various chemical problems. We investigated the feasibility of machine learning models for the prediction of activation energies of gas-phase reactions. Six different models with three different types, including the artificial neural network, the support vector regression, and the tree boosting methods, were tested. We used the structural and thermodynamic properties of molecules and their differences as input features without resorting to specific reaction types so as to maintain the most general input form for broad applicability. The tree boosting method showed the best performance among others in terms of the coefficient of determination, mean absolute error, and root mean square error, the values of which were 0.89, 1.95, and 4.49 kcal mol , respectively. Computation time for the prediction of activation energies for 2541 test reactions was about one second on a single computing node without using accelerators.

A wide variety of data-driven approaches have been introduced in the field of quantum chemistry. To extend the applicable range and improve the prediction power of those approaches, highly accurate quantum chemical benchmarks that cover extremely large chemical spaces are required. Here, we report ~134 k quantum chemical calculations performed with G4MP2, the fourth generation of the G-n series in which second-order perturbation theory is employed. A single composite method calculation executes several low-level calculations to reproduce the results of high-level ab initio calculations with the aim of saving computational costs. Therefore, our database reports the results of the various methods (e.g., density functional theory, Hartree-Fock, Møller–Plesset perturbation theory, and coupled-cluster theory). Additionally, we examined the structure information of both the QM9 and the revised databases via chemical graph analysis. Our database can be applied to refine and improve the quality of data-driven quantum chemical prediction. Furthermore, we reported the raw outputs of all calculations performed in this work for other potential applications.