Ikutaro Hamada

Mechanochemistry uses mechanical forces to drive chemical transformations, offering an energy-efficient alternative to traditional thermal, optical, or electrical reaction pathways. At the single-molecule scale, controlling structural changes with mechanical stimuli is fundamental for developing nanoscale devices and understanding chemical reactivity. In conventional single-molecule reactions driven by light or electrical currents, energy is supplied through one-way excitation processes, requiring the single-event energy input to exceed the activation energy barrier. Here we show that mechanically driven single-molecule reactions can bypass this limitation, proceeding with a net energy cost below the activation energy. By using atomic force microscopy to induce the structural inversion of individual, bowl-shaped sumanene molecules on a gold surface, we measure the mechanical energy spent during the transformation. The net energy cost is significantly lower than the theoretical activation energy. This sub-barrier reaction occurs because attractive van der Waals forces from the mesoscopic tip body stabilize the transition state more effectively than the final state. Our findings demonstrate a distinct energetic mechanism in mechanochemistry, highlighting alternative strategies for energy-efficient molecular manipulation.

Porous materials such as metal-organic frameworks (MOFs) are widely studied for applications in catalysis and gas adsorption, including carbon dioxide capture and hydrogen storage for energy and environmental challenges. Accurate simulation of gas adsorption in these materials remains challenging because most approaches rely on force fields (FFs), which lack first-principles accuracy. Here, we present a hybrid sampling approach, complemented by an active-learning-style refinement step, for generating machine-learning potentials (MLPs) tailored for grand canonical Monte Carlo (GCMC) simulations of gas adsorption. The training dataset combines snapshots from FF-based GCMC sampling, evaluated by single-point density functional theory (DFT) calculations, with trajectories from DFT-based molecular dynamics (MD). We demonstrate this approach for hydrogen adsorption in MOF-303, achieving accurate energy and force evaluation, as well as robust GCMC simulation including on-the-fly quantum corrections for nuclei. The MLP-based calculation also revealed four primary and four secondary adsorption sites formed near N, O, C, and –OH motifs, highlighting the material's structural heterogeneity and adsorption behavior. The resulting MLPs enable calculation of adsorption isotherms, isosteric heat values, and diffusion coefficients across temperatures. This hybrid-sampling framework bridges DFT-level accuracy and larger-scale simulation capability than a b i n i t i o MD, and can in principle be applied to other porous materials and gas types to support the design of materials for gas storage and separation.

Hydrogen storage remains a central bottleneck for scalable hydrogen energy systems due to the multiscale and coupled nature of the thermodynamics, kinetics, and microstructural evolution of hydrogen storage materials (HSMs). Although artificial intelligence (AI) has accelerated materials discovery, current approaches remain constrained by fragmented data, limited physical consistency, and weak integration with experimental validation. Here, we propose a unified framework that integrates coherent data infrastructure, physics-grounded modeling, and AI-driven inverse design within a closed-loop discovery paradigm. By constraining optimization with thermodynamics, kinetics, uncertainty, provenance, and experimental feedback, this approach enables adaptive, physically consistent optimization, thereby establishing a pathway toward autonomous, digital-twin-enabled discovery of HSMs.

The conversion of CO2 into valuable chemicals has attracted considerable attention as a strategy to address two major global challenges: the production of renewable energy and the reduction of CO2 emissions. Experimental studies have demonstrated that hydrogen boride sheets exhibit promising potential for CO2 hydrogenation, producing methane and ethane, while uniquely supplying hydrogen directly from its lattice without requiring external H2. However, the underlying reaction mechanism remains poorly understood. In this study, we employ density functional theory calculations to investigate the reaction pathways of CO2 conversion to CH4 on hydrogen-deficient HB sheets, where H-vacancy sites serve as the active centers for CO2 activation. Our results reveal that the conversion of CO2 proceeds via a CO-mediated dissociative mechanism. We further consider the effect of O* participation as a reactant following C–O bond cleavage and show that coadsorbed OH* lowers the barrier for HCO* formation, making the associative pathway more kinetically favorable than the route proceeding via C*. The rate-determining step is identified as the hydrogenation of CH* to CH2*, with a barrier of 2.02 eV, consistent with the experimentally observed negligible CH4 yield under dry conditions. These findings provide mechanistic insight and complement existing experimental observations.

The existence of the hydroxyl ligand on a single-atom catalyst embedded in graphene, especially Fe(OH)−N4–C, is suggested to improve the oxygen reduction reaction (ORR) from the pristine Fe−N4–C. However, the theoretical electrocatalytic activity of the ORR process is shown to be highly dependent on the electrolyte solution and the applied electrode potential. Herein, we performed the constant-potential simulation and microkinetic modelling to investigate the mechanism of ORR on well-studied Fe(OH)−N4–C and Co(OH)−N4–C catalysts and compared them with the pristine ones, using density functional theory calculation combined with the effective screening medium method and the reference interaction site model. It was found that the Fe(OH)−N4–C and Co(OH)−N4–C have comparable ORR activities to Fe−N4–C and Co−N4–C, and the calculated limiting potentials and half-wave potentials agree with the experimental values, in contrast to the results obtained using the constant-charge method (i.e. under the neutral condition) regardless of consideration of the solvation effect. This article is part of the theme issue ‘Surfaces, interfaces and heterogeneous catalysis’.

Single-atom platinum on graphitic materials can enhance catalysis of the oxygen reduction reaction (ORR). The local atomic structure shapes catalytic activity, but is not yet sufficiently understood due to its complexity. In this study, a machine learning-enhanced global structure search algorithm was combined with density functional theory calculations to explore previously unknown configurations of single-atom platinum adsorbed at graphene edges. Here, some of the novel stable structures demonstrated superior ORR catalytic performance compared to the conventional Pt(111) surface. This article is part of the theme issue ‘Surfaces, interfaces and heterogeneous catalysis’.

Hydrogen carriers that enable efficient transport and on-demand release of molecular hydrogen (H₂) are crucial for practical hydrogen-based energy systems. Hydrogen boride (HB) nanosheets, composed of boron and hydrogen in a 1:1 stoichiometric ratio, have promising potential as safe and lightweight hydrogen carriers owing to their high gravimetric hydrogen density (8.5 wt %). In particular, heating of HB nanosheets results in H₂ release over a broad temperature range from 363 to 1473 K. However, the mechanism of the multimodal H₂ desorption remains unclear. In this work, we elucidate that the interlayer H···H distances (d_H···H) determine the multimodal desorption of H₂ especially in the lower-temperature range (2 pressures were prepared to investigate the thermal stability of their bonding configurations. Infrared spectroscopy and temperature-programmed desorption measurements revealed the occurrence of hydrogen depletion while the bonding configuration remains unchanged. The first to third nearest d_H···H, along with the numbers of the corresponding H···H pairs, were systematically calculated for four possible interlayer stacking types. The calculated H···H pairs distribution closely matched the experimental profile for the thermally induced H₂ desorption, suggesting that the multimodal desorption of H₂ in the lower-temperature range is governed by the distribution of hydrogen distances. This study elucidates the mechanisms of H₂ formation in HB nanosheets and provides valuable insights into the design of two-dimensional hydrogen containing materials.

Porous materials such as metal–organic frameworks (MOFs) are widely studied for applications in catalysis and gas adsorption, including carbon dioxide capture and hydrogen storage for energy and environmental challenges. Accurate simulation of gas adsorption in these materials remains challenging because most approaches rely on classical force fields, which lack first-principles accuracy. Here, we present an approach to generating machine-learning potentials (MLPs) tailored for grand canonical Monte Carlo (GCMC) simulations of gas adsorption. The training dataset combines snapshots from GCMC sampling, evaluated by single-point density functional theory (DFT), with trajectories from DFT-based molecular dynamics. We demonstrate this approach for hydrogen adsorption in MOF-303, achieving accurate energies and forces and robust GCMC performance. The simulation also revealed four primary and four secondary adsorption sites formed near N, O, C, and –OH motifs, highlighting the material’s structural heterogeneity and adsorption behavior. The resulting MLP enables calculation of adsorption isotherms, isosteric heat values, and diffusion coefficients across temperatures. This hybrid-sampling framework bridges DFT-level accuracy and large-scale simulation capability, providing a transferable tool for screening and designing porous materials for gas storage and separation.