Atomic displacement parameters (ADPs) are crystallographic structural data that may represent atomic motion, possible static displacive disorder, and thermal vibration. Thermoelectric materials often contain rattling atoms to improve performance, and ADPs describe how atoms rattle. This paper discusses the ADPs of the Ba2 site of Ba₈Ga₁₆Ge₃₀, a clathrate structure thermoelectric material. A molecular dynamics simulation approach using a neural network potential was applied to Ba₈Ga₁₆Ge₃₀ models considering the disorder of Ga and Ge on the Ga/Ge cage sites. The calculated U is non-zero when extrapolated to 0 K, overestimates experimental U¹¹ by 0.005-0.009 Ų at temperatures between 200 and 300 K, and overestimates experimental U²² by 0.010-0.014 Ų between 15 and 300 K.

The next version of ABINIT-MP, Open Version 2 Revision 12, is scheduled for release in spring 2026. We are also planning Revision 16, which will incorporate GPU acceleration for MP2 energy calculations. In parallel, we are preparing the Version 3 series to support open-shell and multireference systems. Furthermore, development has begun on FMO-X, a new implementation designed for GPU environments from the outset. This paper presents an overview of these developments.

Noncovalent interactions such as hydrogen bonds and π-π interactions are involved in the stabilization of three-dimensional structures and specific molecular recognition. We have recently developed the Negative Fragmentation Approach (NFA) to evaluate noncovalent interactions quantitatively, using quantum chemical calculations. Although NFA is effective for small and medium-sized systems, its direct application to large macromolecules such as proteins demands further refinement. Here, we extended the original NFA to evaluate noncovalent interactions in proteins by combining it with the ONIOM method. The NFA-ONIOM scheme was applied to a hydrogen bond between ∆⁵-3-ketosteroid isomerase and equilenin, an analogue of the intermediate.

Preparing slab models and large supercells for first-principles surface calculations often requires tedious manual editing of atomic coordinates. We developed browser-based tools that provide synchronized coordinate-table editing and interactive 3D visualization (Mol*) for two structure models. The tools implement (i) surface transfer, which copies an optimized surface region from a thin slab to a thicker slab while preserving Cartesian coordinates, (ii) composite-supercell construction based on a user-defined tiling pattern of pre-optimized blocks, and (iii) a Python-based calculator of partial zero-point energies and vibrational enthalpies. The backend is implemented in Python using ASE and pymatgen, and the frontend is built with TypeScript/React.

Although all hexoses share the molecular formula C₆H₁₂O₆, differences in hydroxyl group orientation lead to pronounced variations in crystal packing and hydrogen-bond networks. However, a systematic, site-resolved electronic comparison of intermolecular hydrogen bonding across hexose crystals remains limited. To clarify the electronic features of intermolecular hydrogen bonding, we investigated 13 hexoses using DV-Xα molecular orbital calculations on cluster models constructed from single-crystal structures (CSD and in-house determinations). Hydrogen-bond strength was evaluated by the bond overlap population (BOP), and Full Interaction Maps (FIMs) analysis was performed for β-aldohexoses. BOP analysis showed that the anomeric O1 site gives the largest values in β-aldohexoses, whereas no distinct site preference appears in α-aldohexoses; in contrast, O1 shows the smallest values in ketohexoses. Combined BOP and FIMs analyses indicate that both the strength and site selectivity of intermolecular hydrogen bonding are governed by steric features arising from molecular structure. FIMs analysis further demonstrated that hydrogen-bond formation depends not only on electronic strength but also on spatial arrangement. The present findings provide a quantitative, site-resolved basis for understanding monosaccharide crystal structures and support rational design of rare-sugar-based functional materials by linking hydrogen-bond electronics with packing geometry.

The enzymatic reaction mechanism of the serine protease neuropsin (KLK8) was investigated using QM/MM calculations combined with MD simulations. The out-of-plane distortion of the scissile amide bond in the substrate-binding complex contributed to a reduction in the activation barrier. Nucleophilic attack by Ser195 Oγ initiated proton transfer among the catalytic triad and the formation of a tetrahedral intermediate. Free energy analysis indicated that this intermediate formation is the rate-determining step, emphasizing the importance of amide bond distortion and oxyanion hole stabilization for catalysis.

The μ–Al₄Mn phase is a complex metallic alloy and an approximant of Al–Mn quasicrystals, characterized by vacancy-ordered layered structures. Density functional theory calculations were performed using a simplified single-component aluminum model, in which the Uchida stacking motif emerges spontaneously from the vacancy-ordered A-layer geometry.

Structural optimization yields a stable configuration with interlayer distances close to the golden ratio, indicating a locally icosahedral-like coordination geometry. Electron localization function analysis reveals predominantly metallic bonding with strong local electron localization near vacancies, demonstrating that vacancy ordering and electronic localization stabilize the Uchida stacking motif in the μ–Al₄Mn phase. These results provide insight into how the coexistence of vacancy ordering and local electronic localization may be related to the origin of the brittle behavior observed in the μ–Al₄Mn phase.

To identify lead-free perovskite compounds with high compositional flexibility, we developed a band gap prediction model using machine learning (ML). We analyzed CGCNN input features and evaluated factors contributing to band gap prediction, revealing that B site atomic features were dominant. Based on the analysis results, we designed a highly interpretable feature set that derived from compositional information and applied it to the model for band gap prediction. Furthermore, we trained feature-generation ML models to predict structural features, such as cell volume and B-X bond distance, from compositional information and added these features to a support vector regression (SVR) model. We confirmed that incorporating ML-generated structural features improved the accuracy of band gap prediction.