On the occasion of 20^th year anniversary of computational chemistry society of Japan, based on more than 50 years research experiences in both experimental and computational fields, evolution of research topics and methodology was discussed in terms of social evolution including the importance of environmetal technology and compotational methods. Start of computational chemistry gave significant impact on the progress of research activities of the authors group including many industry/academy collaborations for a variety of social/industrial topics and these collaborations further promoted the progress of computational methodologies to succesfully perform the collaborations. The own idea for the research topics/methodologies is emphasized highly important for younger reserachers in addition to the deep understanding of evolving society and academy.

As COVID-19 continues to spread, most of the science events in Japan have been canceled, postponed or held online. I will introduce that the public events and gatherings of the Society of Computer Chemistry of Japan are also limited, and describe the importance of digital content and social media collaboration, which is also intended to raise public awareness of the role of computational science.

In 1811, A. Avogadro explained that elementary hydrogen exists as H₂ molecules. Avogadro's findings were almost completely ignored until S. Canizarro presented them at the Karlsruhe Conference in 1860. In 1916, G. N. Lewis described the sharing of electron pairs between atoms. This idea provides a picture of covalent bonding, however, quantum mechanics is needed to understand the nature of the chemical bond. In 1927, W. Heitler and F. London were succeeded in the first explanation of the chemical bond of molecular hydrogen based on the valence bond method. Molecular orbital (MO) method was also proposed to explain theoretically the nature of the chemical bond. Similar to photons, electrons show properties of particles and waves. A. Tonomura revealed that the wave character of electron is displayed when a beam of electron is passed through parallel slits thereby creating interference patterns [5, 7, 8]. Formation of two molecular orbitals φ₁ (bonding MO) and φ₂ (antibonding MO) from the interference of two hydrogen atomic orbitals χ₁ and χ₂ is shown in Figure 4. Electron density distribution in the 3-dimensional representation of the squares of hydrogen MOs are given in Figure 5. Difference electron density between |φ₁|² and (|χ₁|² + |χ₂|²) in Figure 6 (c) and 7 (b) shows the internuclear region having high probability densities of finding an electron, to explain electron densities (-δe) in this region could attract the hydrogen nuclei having plus electric charges.

The Society of Computer Chemistry, Japan (SCCJ) was established on January 1, 2002 by merging the Japan Chemistry Program Exchange (JCPE) and the Chemical Software Society of Japan (CSSJ), and will soon celebrate its 20th anniversary. This article looks back on the past 20 years of SCCJ by introducing literatures related to computer chemistry. First, the numbers of literatures are compared by individual research fields such as chemistry, physics, engineering, materials science, and biochemistry. Next, some of highly cited literatures with top 100 rankings are explained with categorizing into methodologies, software, database, and topics.

To look for factors of the COVID-19 spreading in the whole world currently, an empirical study has been tried by using a multi-regression analysis for mortality rates of 47 prefectures as an objective variable, and various indices as the explanatory variables. A support vector machine method was applied to deal with a nonlinear relationship between objective and explanatory variables, and a sensitivity analysis was applied to search the factors of the COVID-19 mortality. Welfare, urbanization, poverty rate, service industry, and sex ratio were obtained as dangerous factors which increase mortality, while single-person households, meals, and sleep were obtained as defensing factors which decrease mortality. Novel and useful knowledge for prevention measure of the COVID-19 was obtained: three factors of urbanization, service industry, and single-person household relating to the Three Cs contribute largest to the mortality, and two factors of welfare and poverty rate, reflecting the reality of the poor people also contribute.

Nanoparticles have a wide range of applications as catalysts. Their catalytic and electronic properties differ from those of materials with flat surfaces and bulk materials. First-principles calculations of real system nanoparticles, which use nanoparticle models based on real shapes extracted from experimental observations, are essential for studying these properties to facilitate the computational design of new catalysts. In this article, we review first-principles studies of models of real systems of monometallic, bimetallic, and supported nanoparticles. The stability, electronic structure, hydrogen absorption behavior, and small molecule adsorption behavior are reviewed, and advances in first-principles calculations of real system nanoparticles are presented. Further, a combination of machine learning and first-principles studies is also considered. Future perspectives are discussed on the basis of these examples.

A scheme to automatically determine the buffer region in the divide-and-conquer (DC) large-scale quantum chemical method is introduced. The buffer region directly relates to the error introduced by the DC method. In the iterative DC Hartree-Fock procedure, the automatic scheme adopts two-layered buffer region and gradually enlarges the buffer region by evaluating the energy contribution from the outer buffer region and determining whether the buffer region should be enlarged or not based on the energy-based threshold. On the other hand, in the non-iterative DC second-order Møller-Plesset perturbation calculation, the energy contribution is approximately estimated for the atoms in the buffer region and only those atoms that contribute more than an energy-based threshold are left in the buffer region. We demonstrated that both methods achieve almost constant accuracy in the energy using only one energy-based threshold as a parameter.

Fisher Discriminant Orthogonal Decomposition (FDOD) is a discriminant analysis method incorporating regularization coefficient and orthogonal decomposition into ordinary Fisher Discriminant Analysis (FDA). This method makes it possible to avoid overfitting in discriminant analyses of multivariate data and to obtain discriminant axes whose number is greater than that of groups. However, FDOD requires long calculation time and large memory. To solve these problems, a novel technique, Fast Fisher Discriminant Orthogonal Decomposition (FFDOD), has been developed. FFDOD saves calculation time and memory by singular value decomposition of the data to be analyzed to remove redundant data. When FFDOD was applied to 275 infrared spectra of 6 types of cellulosic fibers each of which consists of data at 7054 wavenumbers, the calculation time was reduced to 1/84 of that when using FDOD. If the time required for the singular value decomposition is not considered, a remarkable speedup to about 1/290 was realized. The calculation accuracy of FFDOD has been found equivalent with that of FDOD by comparing the results by FFDOD and FDOD.

Models for predicting properties/activities of materials based on machine learning can lead to the discovery of new mechanisms underlying properties/activities of materials. However, methods for constructing models that exhibit both high prediction accuracy and interpretability remain a work in progress because the prediction accuracy and interpretability exhibit a trade-off relationship. In this study, we propose a new model-construction method that combines decision tree (DT) with random forests (RF); which we therefore call DT-RF. In DT-RF, the datasets to be analyzed are divided by a DT model, and RF models are constructed for each subdataset. This enables global interpretation of the data based on the DT model, while the RT models improve the prediction accuracy and enable local interpretations. Case studies were performed using three datasets, namely, those containing data on the boiling point of compounds, their water solubility, and the transition temperature of inorganic superconductors. We examined the proposed method in terms of its validity, prediction accuracy, and interpretability.