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.

An accurate prediction of chemical shifts (δ) to elucidate molecular structures has been a challenging problem. Recently, noble machine learning architectures achieve accurate prediction performance, but the difficulty of building a huge chemical database limits the applicability of machine learning approaches. In this work, we demonstrate that the prior knowledge gained from the simulation database is successfully transferred into the problem of predicting an experimentally measured δ. Although both simulation and experimental databases are vastly different in chemical perspectives, reliable accuracy for δ is achieved by additional training with randomly sampled small numbers of experimental data. Furthermore, the prior knowledge allows us to successfully train the model on the more focused chemical space that the experimental database sparsely covers. The proposed approach, the knowledge transfer from the simulation database, can be utilized to enhance the usability of the local experimental database.

ACE-Molecule (advanced computational engine for molecules) is a real-space quantum chemistry package for both periodic and non-periodic systems. ACE-Molecule adopts a uniform real-space numerical grid supported by the Lagrange-sinc functions. ACE-Molecule provides density functional theory (DFT) as a basic feature. ACE-Molecule is specialized in efficient hybrid DFT and wave-function theory calculations based on Kohn–Sham orbitals obtained from a strictly localized exact exchange potential. It is open-source oriented calculations with a flexible and convenient development interface. Thus, ACE-Molecule can be improved by actively adopting new features from other open-source projects and offers a useful platform for potential developers and users. In this work, we introduce overall features, including theoretical backgrounds and numerical examples implemented in ACE-Molecule.

We developed a program code of configuration interaction singles (CIS) based on a numerical grid method. We used Kohn-Sham (KS) as well as Hartree-Fock (HF) orbitals as a reference configuration and Lagrange-sinc functions as a basis set. Our calculations show that KS-CIS is more cost-effective and more accurate than HF-CIS. The former is due to the fact that the non-local HF exchange potential greatly reduces the sparsity of the Hamiltonian matrix in grid-based methods. The latter is because the energy gaps between KS occupied and virtual orbitals are already closer to vertical excitation energies and thus KS-CIS needs small corrections, whereas HF results in much larger energy gaps and more diffuse virtual orbitals. KS-CIS using the Lagrange-sinc basis set also shows a better or a similar accuracy to smaller orbital space compared to the standard HF-CIS using Gaussian basis sets. In particular, KS orbitals from an exact exchange potential by the Krieger-Li-Iafrate approximation lead to more accurate excitation energies than those from conventional (semi-) local exchange-correlation potentials.

We developed a self-consistent field program based on Kohn-Sham density functional theory using Lagrange-sinc functions as a basis set and examined its numerical accuracy for atoms and molecules through comparison with the results of Gaussian basis sets. The result of the Kohn-Sham inversion formula from the Lagrange-sinc basis set manifests that the pseudopotential method is essential for cost-effective calculations. The Lagrange-sinc basis set shows faster convergence of the kinetic and correlation energies of benzene as its size increases than the finite difference method does, though both share the same uniform grid. Using a scaling factor smaller than or equal to 0.226 bohr and pseudopotentials with nonlinear core correction, its accuracy for the atomization energies of the G2-1 set is comparable to all-electron complete basis set limits (mean absolute deviation ≤1 kcal/mol). The same basis set also shows small mean absolute deviations in the ionization energies, electron affinities, and static polarizabilities of atoms in the G2-1 set. In particular, the Lagrange-sinc basis set shows high accuracy with rapid convergence in describing density or orbital changes by an external electric field. Moreover, the Lagrange-sinc basis set can readily improve its accuracy toward a complete basis set limit by simply decreasing the scaling factor regardless of systems.