Thermal change of lattice parameters of β-LiAlSiO₄ crystal simulated by molecular dynamics simulation was improved by revision of the interatomic potential. The discontinuity of thermal change of c-axis lattice parameter observed in the previous work between 800 K and 900 K was dissolved, but the simulated linear thermal expansion of c-axis was smaller than the reference data. The visualized shift of relative coordinates of each atom with the temperature increase from 300 K to 1200 K showed the different variation between the two types of double helix structures that exist in the unit cell.

Chemical reaction neural network (CRNN) is a machine learning model that enables a data-driven search of chemical reaction mechanisms by incorporating reaction kinetics theory into the neural network architecture. Conventionally, 10³ order data were required for searching a simple reaction system, but the reduction in the number of data required for CRNN is expected to enable its application to various experimental systems. In this study, we investigated the number of data required for prediction in the CRNN. The result showed that prediction of the reaction is possible with as few as 180 data by avoiding falling into local optimal solutions. We also confirmed that incorporating the concept of material balance into loss function has effect of reducing the computational complexity.

We have performed a series of quantum computations for folding of the PSVKMA peptide by using the blueqat AutoQML simulator by which a given problem can be converted from QUBO (quadratic unconstrained binary optimization) of quantum annealing to QAOA (quantum approximate optimization algorithm) of VQE (variational quantum eigensolver). The IonQ quantum system of ion-trap type was utilized as well. A three qubit problem was successful by both. However, the situation became difficult for a five qubit case, especially for the IonQ having vulnerability to noises.

An application for molecular dynamics (MD) simulations was implemented on a smartphone for utilizing virtual reality (VR) in chemistry education. This application consists only of a smartphone, a simple VR lens, and an on-board camera. The screen displays molecular motions equivalent to MD calculations in actual research. The molecules move synchronously with the six-dimensional movements of the user's head. The on-board camera also recognizes hand coordinates, allowing the user to "touch" and "grab" the molecules through the hand model displayed in the VR space. This application enables users to intuitively understand molecular motion. A special lecture was held at high schools using this application, and survey results shows that students' understanding of molecules improved.

In recent years, artificial photosynthesis has attracted attention as a mechanism for generating fuel by sunlight irradiation of a photocatalyst to split water into O and H. Elucidation of the four-electron oxidation reaction mechanism of this split is essential for the development of highly active photocatalysts. A reaction mechanism has been proposed for Co₃O₄ catalysts using time-resolved Fourier transform infrared spectroscopy to identify the intermediates in the oxidation of water under reaction. In this study, density functional theory was used to examine the mechanism of the four-electron reaction of the Co₃O₄ photocatalysts.

Hybrid in silico drug discovery was performed by combining large-scale quantum molecular dynamics (QMD) simulations with the conventional in silico drug discovery, focusing on developing covalent inhibitors against the main protease (M^pro) of SARS-CoV-2, the virus responsible for ongoing COVID-19 pandemic. The crystal structures and instantaneous structures obtained from the large-scale QMD simulations for M^pro were used as receptors in ensemble docking to estimate the binding affinities of the four ligands: the natural substrate recognized by M^pro, that recognized by the other enzyme of SARS-CoV-2, approved covalent inhibitor (PF-07321332), and the new candidate compound X determined from virtual screening. The present result shows that the binding affinity of X was comparable to that of PF-07321332, demonstrating the potency of our drug discovery.

Density functional theory (DFT) is a successful theory for calculating the electronic structure of atoms, molecules, and solids. However, in modern computing environments is difficult significantly improve the computational efficiency of DFT. Acceleration of DFT requires optimization of the computational algorithms. We demonstrate two acceleration methods by optimization of the computational algorithms that optimize parallel parameters for eigenvalue calculations and optimization of convergence conditions for the self-consistent field (SCF) calculation.

π-conjugated carbon molecules such as fullerenes and carbon nanotubes have attracted great interest because of their potential applications as electronic materials and catalysts. Among these molecular frameworks, fullerene substructures of sumanene molecules with C_3v symmetry are expected to form vertical columnar crystal structures and exhibit a range of conduction properties. Various syntheses of molecules with extended π-conjugation at the benzyl position of sumanene have now been reported. However, there are no examples of open-shell systems with extended sumanene features. In this study, we use the results of DFT calculations to discuss the relationship between molecular conformational symmetry and spin multiplicity in a new, extended open-shell system of sumanenes by adding a phenyl group at the benzyl position.

In recent years, the remarkable advances in artificial intelligence technology have led to digital transformation (DX) in various fields. The automated construction of laboratory notebook through filming experiments is a promising application of image recognition for chemistry． In this study, we created an image dataset of chemical experiment, which contains 2376 images and consists of 7 classes of objects. Object detection methods and a multiple object tracking method were implemented and assessed using the dataset toward to develop automated laboratory notebook system.

In the cooling system of sodium-cooled fast reactors, it is required to identify and rapidly detect hydrogen explosively produced by the sodium-water reaction when a water leak occurs due to damage of a heat exchanger tube in a steam generator (SG), in contrast to low-concentration background hydrogen permeating through SG tubes during normal operation. In the present study, we focus on the difference between the background hydrogen and the hydrogen generated by the sodium-water reaction, and theoretically estimate the hydrogen behavior in liquid sodium by using computational chemistry methods. We find that dissolved H or NaH, rather than H₂, is the predominant form of the background hydrogen in liquid sodium, and that hydrogen produced in large amounts by the sodium-water reaction can exist stably as fine bubbles with a NaH layer on their surface.