Photoluminescent coordination nanosheets (CONASHs) comprising three-way terpyridine (tpy) ligands and zinc(II) ions are created by allowing the two constitutive components to react with each other at a liquid/liquid interface. Taking advantage of bottom-up CONASHs, or flexibility in organic ligand design and coordination modes, we demonstrate the diversity of the tpy-zinc(II) CONASH in structures and photofunctions. A combination of 1,3,5-tris[4-(4'-2,2':6',2″-terpyridyl)phenyl]benzene (1) and Zn(BF) affords a cationic CONASH featuring the bis(tpy)Zn complex motif (1-Zn), while substitution of the zinc source with ZnSO realizes a charge-neutral CONASH with the [Zn(μ-OSO)(tpy)] motif [1-Zn(SO)]. The difference stems from the use of noncoordinating (BF) or coordinating and bridging (SO) anions. The change in the coordination mode alters the luminescence (480 nm blue in 1-Zn; 552 nm yellow in 1-Zn(SO)). The photophysical property also differs in that 1-Zn(SO) shows solvatoluminochromism, whereas 1-Zn does not. Photoluminescence is also modulated by the tpy ligand structure. 2-Zn contains triarylamine-centered terpyridine ligand 2 and features the bis(tpy)Zn motif; its emission is substantially red-shifted (590 nm orange) compared with that of 1-Zn. CONASHs 1-Zn and 2-Zn possess cationic nanosheet frameworks with counteranions (BF), and thereby feature anion exchange capacities. Indeed, anionic xanthene dyes were taken up by these nanosheets, which undergo quasi-quantitative exciton migration from the host CONASH. This series of studies shows tpy-zinc(II) CONASHs as promising potential photofunctional nanomaterials.

Machine learning has become more and more popular in computational chemistry, as well as in the important field of spectroscopy. In this concise review, we walk the reader through a short summary of machine learning algorithms and a comprehensive discussion on the connection between machine learning methods and vibrational spectroscopy, particularly for the case of infrared and Raman spectroscopy. We also briefly discuss state-of-the-art molecular representations which serve as meaningful inputs for machine learning to predict vibrational spectra. In addition, this review provides an overview of the transferability and best practices of machine learning in the prediction of vibrational spectra as well as possible future research directions.

Strong electron correlation effects are one of the major challenges in modern quantum chemistry. Polynuclear transition metal clusters are peculiar examples of systems featuring such forms of electron correlation. Multireference strategies, often based on but not limited to the concept of complete active space, are adopted to accurately account for strong electron correlation and to resolve their complex electronic structures. However, transition metal clusters already containing four magnetic centers with multiple unpaired electrons make conventional active space based strategies prohibitively expensive, due to their unfavorable scaling with the size of the active space. In this work, forefront techniques, such as density matrix renormalization group (DMRG), full configuration interaction quantum Monte Carlo (FCIQMC), and multiconfiguration pair-density functional theory (MCPDFT), are employed to overcome the computational limitation of conventional multireference approaches and to accurately investigate the magnetic interactions taking place in a [Co(II)3Er(III)(OR)4] (chemical formula [Co(II)3Er(III)(hmp)4(μ2-OAc)2(OH)3(H2O)], hmp = 2-(hydroxymethyl)-pyridine) model cubane water oxidation catalyst. Complete active spaces with up to 56 electrons in 56 orbitals have been constructed for the seven energetically lowest different spin states. Relative energies, local spin, and spin–spin correlation values are reported and provide crucial insights on the spin interactions for this model system, pivotal in the rationalization of the catalytic activity of this system in the water-splitting reaction. A ferromagnetic ground state is found with a very small, ∼50 cm–1, highest-to-lowest spin gap. Moreover, for the energetically lowest states, S = 3–6, the three Co(II) sites exhibit parallel aligned spins, and for the lower states, S = 0–2, two Co(II) sites retain strong parallel spin alignment.

A proper treatment of electron correlation effects is indispensable for accurate simulation of compounds. Various post-Hartree-Fock methods have been adopted to calculate correlation energies of chemical systems, but time complexity usually prevents their usage in a large scale. Here, we propose a density functional approximation, based on machine learning using neural networks, which can be readily employed to produce results comparable to second-order Møller-Plesset perturbation (MP2) ones for organic compounds with reduced computational cost. Various systems have been tested and the transferability across basis sets, structures, and nuclear configurations has been evaluated. Only a small number of molecules at the equilibrium structure has been needed for the training, and generally less than 5% relative error has been achieved for structures outside the training domain and systems containing about 140 atoms. In addition, this approach has been applied to make predictions on correlation energies of nuclear configurations extracted from density functional theory-based molecular dynamics trajectories with only one or two structures as training data.

Water nucleophilic attack is an important step in water oxidation reactions, which have been widely studied using density functional theory (DFT). Nevertheless, a single-determinant DFT picture may be insufficient for a deeper insight into the process, in particular during the oxygen-oxygen bond formation. In this work, we use complete active space self-consistent field calculations and describe an approach for a complete active space analysis along a reaction pathway. This is applied to the water nucleophilic attack at a Ru-based catalyst, which has successfully been used for efficient water oxidation and in silico design of new water oxidation catalysts recently. K E Y W O R D Scomplete active space, multiconfigurational method, reaction pathway, water nucleophilic attack, water oxidation