論文

Metal-encapsulating germanium cage nanoclusters, M@Ge16 (M = lutetium (Lu) and tantalum (Ta)), consist of a single metal atom enclosed within a Ge16 cage and can be regarded as binary cage superatoms (BCSAs) exhibiting enhanced geometric and electronic stability due to dense atomic packing and closed-shell electron configurations. In this study, Lu@Ge16 and Ta@Ge16 were deposited onto hexa-tert-butyl-hexa-peri-hexabenzocoronene (HB-HBC)- and fullerene (C60)-modified highly oriented pyrolytic graphite (HOPG) substrates, respectively. Their chemical stability against O2 was evaluated by X-ray photoelectron spectroscopy (XPS) and ultraviolet photoelectron spectroscopy (UPS). Lu@Ge16– on HB-HBC remains intact even after 30 L of O2 exposure, demonstrating better oxidation resistance than the Si16 analogue Lu@Si16. Ta@Ge16+ on C60 undergoes oxidation after exposure to 1.0 × 104 L of O2, indicating cage degradation and partial metal oxidation, although its initial oxidation resistance is comparable to that of Ta@Si16+ on C60. Together with the better oxidative resistivity of Y@Ge16 relative to Y@Si16, these results suggest that the enhanced robustness of Ge-based BCSAs arises from a better spatial match between the larger Y and Lu atoms and the Ge16 cage. This study highlights the critical role of geometric packing in achieving chemically resilient and electronically tunable designer BCSAs.

The hydration of magnesium oxide (MgO) to magnesium hydroxide (Mg(OH)2) is a fundamental solid-surface chemical reaction with significant implications for materials science. Yet its molecular-level mechanism from water adsorption to Mg(OH)2 nucleation and growth remains elusive due to its complex and multistep nature. Here, we elucidate the molecular process of MgO hydration based on structures of the MgO/water interface obtained by a combined computational chemistry approach of potential-scaling molecular dynamics simulations and first-principles calculations without any a priori assumptions about reaction pathways. The result shows that the Mg2+ dissolution follows the dissociative water adsorption. We find that this initial dissolution can proceed exothermically even from the defect-free surface with an average activation barrier of ∼12 kcal/mol. This exothermicity depends crucially on the stabilization of the resulting surface vacancy, achieved by proton adsorption onto neighboring surface oxygen atoms. Further Mg2+ dissolution then occurs in correlation with proton penetration into the solid. Moreover, we find that the Mg(OH)2 nucleation and growth proceeds according to the dissolution–precipitation mechanism, rather than a solid-state reaction mechanism involving a direct topotactic transformation. In this process, Mg2+ ions migrate away from the surface and form amorphous Mg–OH chains as precursors for Mg(OH)2 nucleation. We also demonstrate that sufficient water facilitates the formation of more ordered crystalline nuclei. This computational study provides a comprehensive molecular-level understanding of MgO hydration, representing a foundational step toward elucidating the mechanisms of this class of complex and multistep solid-surface chemical reactions.

Polymer informatics, which involves applying data-driven science to polymers, has attracted considerable research interest. However, developing adequate descriptors for polymers, particularly copolymers, to facilitate machine learning (ML) models with limited datasets remains a challenge. To address this issue, we computed sets of parameters, including reaction energies and activation barriers of elementary reactions in the early stage of radical polymerization, for 2500 radical-monomer pairs derived from 50 commercially available monomers and constructed an open database named “Copolymer Descriptor Database”. Furthermore, we built ML models using our descriptors as explanatory variables and physical properties such as the reactivity ratio, monomer conversion, monomer composition ratio, and molecular weight as objective variables. These models achieved high predictive accuracy, demonstrating the potential of our descriptors to advance the field of polymer informatics.

Conventional molecular geometry searches on a potential energy surface (PES) utilize energy gradients from quantum chemical calculations. However, replacing energy calculations with noisy quantum computer measurements generates errors in the energies, which makes geometry optimization using the energy gradient difficult. One gradient-free optimization method that can potentially solve this problem is Bayesian optimization (BO). To use BO in geometry search, an acquisition function (AF), which involves an objective variable, must be defined suitably. In this study, we propose a strategy for geometry searches using BO and examine the appropriate AFs to explore two critical structures: the global minimum (GM) on the singlet ground state (S0) and the most stable conical intersection (CI) point between S0 and the singlet excited state. We applied our strategy to two molecules and located the GM and the most stable CI geometries with high accuracy for both molecules. We also succeeded in the geometry searches even when artificial random noises were added to the energies to simulate geometry optimization using noisy quantum computer measurements.

This study focuses on optimizing free-radical copolymerization in a flow system, emphasizing control over monomer composition—a key factor in industrial applications that require homogeneous products and high conversion. Tubes of varying lengths (2.5 m, 5 m, 7.5 m, 10 m) were used to investigate time-dependent variables under random polymerization conditions. Using a method previously shown to achieve precise 50:50 copolymer compositions with styrene and methyl methacrylate, the same was applied in flow conditions. While copolymer composition remained stable, monomer conversion rates varied with tube length and molecular weights were comparable, demonstrating the process’s industrial relevance and tunability.

C-Glycosyltransferases have garnered attention owing to their ability to synthesize C-glycosides with high conversion and selectivity in one-pot reactions. Their potential in rational enzyme engineering makes them valuable for the synthesis of diverse C-glycosides. However, the detailed reaction mechanism remains unclear. To address this, we investigated the C-glycosylation of phloretin catalyzed by the glycosyltransferase GgCGT in the presence of the coenzyme UDP-glucose. Using density functional theory (DFT) calculations on a cluster model, we identified the most favorable pathway for C-glycosylation. The reaction proceeds via an initial proton transfer from phloretin to UDP-glucose, followed by the nucleophilic attack of phloretin on the glucose moiety and subsequent dissociation of UDP in an SN2-like manner. The SN2 step yields a non-aromatic intermediate, which can be rapidly converted to C-glycoside even without an enzymatic environment. The key residue that facilitates the rate-determining SN2 step is His-27, which stabilizes phloretin via hydrogen bonding. Additionally, to clarify why alternative products such as O-glycosides are not formed, we also investigated the O-glycosylation pathway. Our calculations revealed that O-glycosylation was promoted by proton transfer from UDP-glucose, like C-glycosylation, but was suppressed by structural fixation due to hydrogen bonding among phloretin, glucose, and GgCGT.

While robotic automation has demonstrated remarkable performance, such as executing hundreds of experiments continuously over several days, designing synchronized motions between the robot and experimental jigs remains challenging, especially for flexible experimental automation. This challenge stems from the fact that even minor changes in experimental conditions often require extensive reprogramming of both robot motions and jig control commands. Previous systems lack the flexibility to accommodate frequent updates, limiting their practical utility in actual laboratories. To update robotic automation systems flexibly by chemists, we propose a concept that enables the automation of experiments by utilizing dual demonstrations of robot motions and jig operations by chemists. To verify this concept, we developed a chemical-experiment-automation system consisting of jigs to assist the robot in experiments, a motion-demonstration interface, a jig-control interface, and a mobile manipulator. We validate the concept through polymer-synthesis experiments, focusing on critical liquid-handling tasks such as pipetting and dilution. The experimental results indicate high reproducibility of the demonstrated motions and robust task-success rates. This comprehensive concept not only simplifies the robot programming process for chemists but also provides a flexible and efficient solution to accommodate a wide range of experimental conditions, providing a practical framework for intuitive and adaptable robotic laboratory automation. Our project page is available at: https://sasakihikaru.github.io/Chemical-Experiment-Automation-with-Dual-Demonstration/.

The exploration of minimum energy crossing points (MEXs) between potential energy surfaces (PESs) is essential for understanding nonradiative decay mechanisms and plays a key role in the design of photofunctional materials. In lanthanide (Ln3+) complexes, however, the presence of open-shell 4fN electrons leads to quasi-degenerate electronic states, making MEX searches particularly challenging. To describe the PESs of 4f-5d or charge-transfer excited states (i.e., 4fN−1X excited states) of Ln3+ complexes, we propose a new approximation, the ion energy shift (IES) method. In this approach, the 4fN−1X excited state is represented using density functional theory (DFT) with the large-core relativistic effective core potential (RECP) for Ln4+, which has a higher formal charge than the actual ion (Ln3+), and the PES is shifted to reproduce the target excitation energy. In this study, we validate the IES method against the multiconfigurational wavefunction results and apply it to elucidate the origin of the different excited-state lifetimes of hydrated Ce3+ complexes with and without coordination of a carboxylate ligand.