論文

Exfoliation is a common route to synthesize 2D materials such as monolayered and few-layered nanosheets. However, it is not easy to control the breaking-down process. This work shows a new exfoliation strategy for generation of nanosheets from soft layered composites in high yield. Soft layered composites of inorganic hosts and organic guests are exfoliated into nanosheets in organic dispersion media. A simple yield prediction model is constructed on small-scale experimental data by sparse modeling, a data-scientific approach, combined with chemical perspective. The prediction model indicates the guest–medium combinations enabling high- and low-yield syntheses of the nanosheets. The high- and low-yield syntheses are actually achieved on different types of layered transition metal oxides in a minimum number of experiments. The number of experiments is reduced by 94%. The experimental optimization realizes further improvement of the yield, namely, shortening the exfoliation time. The present strategies can be applied to obtain a variety of 2D materials through exfoliation. Moreover, the present work suggests that combination of small experimental data, data-scientific approach, and chemical perspective, i.e., experiment-oriented materials informatics, has potentials for acceleration of materials design.

High-performance organic energy storage has attracted much interest as a future battery. Organic anode has been developed as an alternate of graphite in the past decade. However, the design strategies are not fully studied for further development. The present work shows experiment-oriented materials informatics (MI) for efficient exploration of design strategy and new compounds for an active material of high-performance organic anode. A few important factors to achieve high specific capacity are extracted from training dataset containing experimentally measured specific capacity, calculation results, and literature data of the model compounds using sparse modeling, an informatics approach. Although the prediction model is not sufficiently accurate, the model assists in exploration of new compounds in combination with experience and intuition of experimental scientists. New compounds with high specific capacity, such as 227 mA h g–1 at 100 mA g–1 for benzo[1,2-b:4,5-b′]dithiophene (BdiTp), are efficiently discovered in a minimum number of experiments. Furthermore, polymerization of BdiTp exhibits the enhanced performances, such as 933 mA h g–1 at 20 mA g–1 and cycle stability, and rate performance. MI combined with experiment, calculation, and data accelerates design new materials and functions by experimental scientists having their small data, experience, and intuition.

Exfoliation is a general route to obtain two-dimensional (2D) nanomaterials. A variety of methods have been developed for controlled exfoliation of layered materials based on stacking via van der Waals interaction, such as graphite and transition-metal dichalcogenides. On the other hand, rigid layered materials consisting of inorganic layers and interlayer metal ions stacked via electrostatic interaction, such as transition-metal oxides and clays, have a limited number of exfoliation methods. Here we studied a new exfoliation route through formation of soft layered composites. Intercalation of guest organic molecules changed rigid inorganic layered compounds into soft layered composites with stacking via van der Waals interaction. The soft layered inorganic-organic composites were exfoliated into surface-modified nanosheets in organic media. The layered composites showed swelling with dispersion in organic media. The time-course analyses suggest that the layered composites were simultaneously exfoliated in the vertical direction and fractured in the lateral direction. Thinner and smaller nanosheets were obtained with an increase in the exfoliation time. Although the resultant nanosheets gradually aggregated in the colloidal liquid, the original dispersion state was recovered with sonication for 5 min at room temperature. This exfoliation route using soft layered composites can be used in the synthesis and application of a variety of 2D nanomaterials.

Layered materials, alternate stackings of two or more components, are found in a wide range of scales. Chemists can design and synthesize layered structures containing functional units. The soft-type layered materials exhibit characteristic dynamic functions originating from two-dimensional (2D) anisotropy and structure flexibility. This feature article focuses on "intercalation"and "flexibility"as two new perspectives for designing soft layered materials. Intercalation of guests is a characteristic approach for design of layered structures. Flexibility is an important factor to control the dynamic functions of the layered structures. As a model case, the intercalation-induced tunable stimuli-responsive color-change properties of layered polydiacetylene (PDA) are introduced to study the impact of the intercalation and flexibility on the dynamic functions. Recently, layered materials have drastically expanded the research area from conventional rigid inorganic compounds to new self-assembled nanostructures consisting of organic components, such as polymers, metal-organic frameworks, and covalent-organic frameworks. These new layered architectures have potentials for exhibiting dynamic functions originating from the structure flexibility beyond the static properties originating from classical intercalation and host-guest chemistries. Therefore, intercalation and flexibility chemistries of soft layered materials are regarded as new perspectives for design of advanced dynamic functional materials. This journal is

A wide variety of nanosheets including monolayers and few-layers have attracted much interest as two-dimensional (2D) materials for the unique anisotropic structures and properties. On the other hand, one of the significant remaining and challenging issues is the lateral-size control. Since 2D materials are generally synthesized by the exfoliation of layered materials, the lateral size is not easily controlled in the breaking-down processes. The experimental factors have not been found for the control and prediction. In the present work, lateral sizes of the exfoliated transition-metal-oxide nanosheets were predicted and controlled by the assistance of machine learning. Layered composites of host inorganic layers and guest organic molecules were exfoliated into nanosheets in organic dispersion media. The lateral size of the nanosheets was estimated by dynamic light scattering (DLS), instead of microscopy methods, to achieve high-throughput analyses. Factors governing the lateral size are explored on the small experimental data by the assistance of sparse modeling, a method of machine learning. The eight physicochemical parameters of the organic guests and dispersion media were extracted by sparse modeling for the construction of the size-prediction model. The size-prediction model accelerated the selective syntheses of the nanosheets with large and small lateral sizes in a limited number of experiments. The results indicate that the prediction model is a guideline to find suitable exfoliation conditions for size control. Size-selective syntheses of a variety of 2D materials can be achieved by similar methods using sparse modeling on small experimental data. Moreover, sparse modeling is applicable to control the design and exploration of other materials and their processing based on small data. This journal is

Application of data-scientific approaches to conventional sciences, such as chemo-informatics, bio-informatics, and materials informatics (MI), has attracted much interest toward datadriven research. The concept enables accelerated discovery of new materials, enhancement of performance, and optimization of processes. However, sufficient bigdata is not always prepared to apply to machine learning. For example, experimental scientists have their own small data including success and failure in their laboratory, whether in academia or industry. If such small data is effectively utilized with a data-scientific approach, research activities can be accelerated without energy, resource, and cost consumption. This account focuses on MI for small data, a recent concept for application of small data, with introduction of model cases, such as control of exfoliation processes to obtain 2D materials. Combination of machine learning and chemical perspective is effective for construction of straightforward and interpretable predictors through the extraction of a limited number of descriptors from small dataset. Although the prediction accuracy is not so precise, the model has enough accuracy to be a guideline reducing the number of the next experiments. The present MI for small data opens potentials of small-data-driven chemistry and materials science.