Thermosetting resins are one of the most widely used functional materials in industrial applications. Although some of the physical properties of thermosetting resins are controlled by changing the functional groups of the raw materials or adjusting their mixing ratios, it was conventionally challenging to construct machine learning (ML) models, which include both mixing ratio and chemical information such as functional groups. To overcome this problem, we propose a machine learning approach based on extended circular fingerprint (ECFP) in this study. First, we predicted the classification of raw materials by the random forest, where ECFP was used as the explanatory variable. Then, we aggregated ECFP for each classification predicted by the random forest. After that, we constructed the prediction model by using the aggregated ECFP, feature quantities of reaction intermediates, and curing conditions of resin as explanatory variables. As a result, the model was able to predict in high accuracy (R^2 = 0.8), for example, the elastic modulus of thermosetting resins. Furthermore, we also show the result of verification of prediction accuracy in first step, such as using the one-hot-encording. Therefore, we confirmed that the properties of thermosetting resins could be predicted using mixed raw materials by the proposed method.

Design processes of functional polymers were accelerated by adopting the Bayesian optimization; the number of trials in the process was substantially reduced. The optimization process was more than forty time accelerated to find out the target polymer compared to the random selection. The optimization efficiency was found to be successfully improved by utilizing the standard deviation of predicted probability distribution of objective function. The performance of the method was robust for dataset size in the analysis; the target polymer could be found even for a small training dataset. The proposed method is a promising tool for the high-performance polymer design, and a wide range of its applications will be expected in the polymer industry.

High-throughput immuno-electron microscopy is required to capture the protein–protein interactions realizing physiological functions. Atmospheric scanning electron microscopy (ASEM) allows in situ correlative light and electron microscopy of samples in liquid in an open atmospheric environment. Cells are cultured in a few milliliters of medium directly in the ASEM dish, which can be coated and transferred to an incubator as required. Here, cells were imaged by optical or fluorescence microscopy, and at high resolution by gold-labeled immuno-ASEM, sometimes with additional metal staining. Axonal partitioning of neurons was correlated with specific cytoskeletal structures, including microtubules, using primary-culture neurons from wild type Drosophila, and the involvement of ankyrin in the formation of the intra-axonal segmentation boundary was studied using neurons from an ankyrin-deficient mutant. Rubella virus replication producing anti-double-stranded RNA was captured at the host cell’s plasma membrane. Fas receptosome formation was associated with clathrin internalization near the surface of primitive endoderm cells. Positively charged Nanogold clearly revealed the cell outlines of primitive endoderm cells, and the cell division of lactic acid bacteria. Based on these experiments, ASEM promises to allow the study of protein interactions in various complexes in a natural environment of aqueous liquid in the near future.

A molecular dynamics (MD) simulation was performed to study the interaction volume of electron beam in carbon nanomaterials. The interaction between incident electron and carbon atom in the target materials during electron irradiation is introduced by the relativistic binary collision theory. The motion of each atom in the material under electron irradiation is calculated with the MD simulation. The primary energy dependence of the interaction volume in the carbon nanotube and the multi-layered graphene are studied. The secondary damages caused by the knock-on atoms are also discussed.

A molecular dynamics simulation was performed to study the polymer filling process in nanoimprint lithography for a bi-layered resist. The bi-layered resist consisted of PMMA resins with different molecular weights. When the mold cavity size became smaller than the polymer size of the top layer resist, the required force to fill the cavity became large. The molecular weight of the top layer dominated the filling characteristics in the bi-layered resist process.

A theoretical study of resolution in nanoimprint lithography (NIL) has been carried out using molecular dynamics (MD) simulation. We have performed a MD simulation for glass NIL, monitored the friction force during entire NIL process and evaluated the deformed shapes of glass patterns after the mold releasing. The resolution in NIL is governed by the maximum tensile stress acting on the glass, which is induced by the friction force during the mold releasing. Based on the distribution of average number density of atoms in the molded glass, the ultimate resolution in the glass NIL has been proved to be 0.4 nm.

Molecular dynamics (MD) studies are carried out to investigate the electron irradiation damages in carbon nanomaterials. The interaction between an incident electron and a carbon atom is modeled based on the Monte Carlo method using the elastic scattering cross section. The electron irradiation damages in graphen, graphite, single-walled carbon nanotube (SWNT) and carbon nanopeapod are demonstrated. The cross-links among the nanostructures caused by the knock-on effect are observed as typical damages. The dependence of the damages on the electron primary energy is also shown for the SWNT.