Lateral-size control of exfoliated transition-metal–oxide 2D materials by machine learning on small data

Mizuguchi, Ryosuke; Igarashi, Yasuhiko; Imai, Hiroaki; Oaki, Yuya

doi:10.1039/d0nr08684c

Cited by 23 publications

(54 citation statements)

References 55 publications

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“…The lateral-size distribution of the transitionmetal-oxide nanosheets was estimated from the particle-size distribution by dynamic light scattering (DLS) without the use of time-and effort-consuming microscopy techniques. In previous works, [41,44,59,60] DLS was used to facilitate the high-throughput rough estimation of the lateral size. Whereas DLS analysis assumes 3D isotropic spherical morphology, the nanosheets are 2D anisotropic shape.…”

Section: High-throughput Collection Of Data About Lateral-size Distributionmentioning

confidence: 99%

“…Our group has proposed a potential synthetic approach to control the exfoliation. [20,[38][39][40][41][42][43][44] The soft layered composites of metaloxide host layers and interlayer organic guests are exfoliated into the surface-modified nanosheets in dispersion media (Figure 1a). The exfoliation behavior, such as the yield and size of the nanosheets, was changed by the host-guest-medium combinations.…”

Section: Introductionmentioning

confidence: 99%

“…The exfoliation behavior, such as the yield and size of the nanosheets, was changed by the host-guest-medium combinations. [20,41,43,44] In the present work, a straightforward prediction model for the lateral-size distribution was constructed by machine learning on our own small experimental data (Figure 1b,c). The exfoliated nanosheets with monodispersed and polydispersed lateral sizes were selectively obtained by the prediction on unknown host-guest-medium combinations in a limited number of experiments (Figure 1d).…”

Section: Introductionmentioning

confidence: 99%

“…[51][52][53] Our group has focused on combination of sparse modeling and chemical perspective for utilization of small data. [41,43,44,[54][55][56] In sparse modeling, the whole of high-dimensional data are explained by a limited number of strongly correlated factors, namely descriptors, on the assumption of the sparseness. [57,58] In materials science, it means that the target materials and properties can be mainly predicted by a limited number of the descriptors, such as experimental parameters and structural factors.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, combination of sparse modeling and our perspective facilitates extraction of a limited number of interpretable descriptors for construction of a straightforward prediction model. [43,44,54] In the present work, the combination approach was applied to predict the lateral-size distribution of the exfoliated nanosheets (Figure 1). The descriptors of the lateral-size distribution were extracted by combination of machine learning and chemical perspective on the small experimental data (Figure 1c).…”

Section: Introductionmentioning

confidence: 99%

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Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling

Haraguchi

Igarashi

Imai

et al. 2021

Advcd Theory and Sims

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2D materials exhibit emergent properties originating from their characteristic nanostructures. In general, monolayered and few-layered nanosheets are obtained by exfoliation of the precursor layered materials. However, lateral-size distribution of the nanosheets is not easily controlled through the exfoliation because of the unpredictable down-sizing processes. The present work shows selective syntheses of transition-metal-oxide nanosheets with the monodisperse and polydisperse lateral sizes by the assistance of machine learning on small experimental data. The precursor layered composites of host transition-metal oxides and interlayer organic guests are exfoliated into the surface-modified nanosheets in organic dispersion media. A prediction model of the size distribution is constructed by sparse modeling, a method of machine learning, on the experimental data. The host-guest-medium combinations achieving the monodisperse and polydisperse lateral sizes are recommended by the prediction model. Therefore, the nanosheets with the controlled lateral-size distribution are selectively obtained in a limited number of the experiments. Moreover, self-assembly of the polydispersed nanosheets forms the homogeneous thin film exhibiting interference color. The prediction model and its construction method can be applied to the other 2D materials. Moreover, the present work implies that sparse modeling is an effective approach for small-data-driven materials science.

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Section: High-throughput Collection Of Data About Lateral-size Distributionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling

Haraguchi

Igarashi

Imai

et al. 2021

Advcd Theory and Sims

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Nanosheet‐Based Uneven Thin Film Exhibiting Angle‐Independent Structural Color and Its Application to Detection of Biogenic Amines

Haraguchi

Imai

Oaki

2022

Adv Materials Inter

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processes in the liquid phase. In general, monolayer nanosheets are extracted only from exfoliated materials through purification. The unique properties of monolayers have been extensively studied in previous works. [1,2,4,6,[8][9][10] In contrast, other exfoliated nanosheets thicker than monolayers, such as few-layers or multiple-layers, were not widely used for applications, such as sensors and catalysts. At the same time, such materials also have potential applications. [16][17][18][19][20] Therefore, the size, surface chemistry, and assembly state of nanosheets, including monolayers and few-layers, are important for device fabrication. Our group has previously studied the exfoliation of layered transition metal oxides in organic dispersion media through the formation of layered composites with the intercalation of organic guests. [21,22] Surface-modified nanosheets were obtained by exfoliation in an organic medium. The prediction models for the yield, size, and size distribution were constructed through the extraction of the descriptors by a combination of machine learning and chemical insight on small experimental data. [23][24][25][26][27][28] These predictors contributed to the efficient exploration of appropriate experimental conditions, such as host-guest-medium combinations that yield the monodispersed and polydispersed nanosheets. [26] In the present work, surface-modified nanosheets with polydispersed lateral sizes and thicknesses were used to prepare an uneven thin film exhibiting an angle-independent structural color (Figure 1). The corresponding host-guest-medium combinations were selected based on the prediction model.Exfoliated nanosheets have been used as building blocks for the production of composites, [29][30][31][32][33][34][35] such as layer-by-layer assemblies, gels, and organic-inorganic composites. However, nanosheets are not easy to organize into the aligned states, such as thin films and liquid crystals. Specific methods and optimized conditions are required to align the nanosheets because the evaporation of the dispersion media causes random aggregation. Thin films and liquid-crystalline assemblies exhibiting structural colors have been previously prepared by homogeneous stacking of monolayered nanosheets. [35][36][37][38][39][40][41] Structural colors have been applied in the detection of chemical vapors and mechanical stresses. [37][38][39]41] However, self-assembled thin films with homogeneous thickness exhibit angle-dependent structural colors. In previous works, the angle-independent structural color was

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When Machine Learning Meets 2D Materials: A Review

Lu,

Xia,

Ren

et al. 2024

Advanced Science

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The availability of an ever‐expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi‐dimensional parameter space and massive data sets involved is emblematic of complex, resource‐intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data‐driven approach and subset of artificial intelligence, is a potential game‐changer, enabling a cheaper – yet more efficient – alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine‐assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

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Lateral-size control of exfoliated transition-metal–oxide 2D materials by machine learning on small data

Abstract: 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...

Cited by 23 publications

References 55 publications

Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling

Size‐Distribution Control of Exfoliated Nanosheets Assisted by Machine Learning: Small‐Data‐Driven Materials Science Using Sparse Modeling

Nanosheet‐Based Uneven Thin Film Exhibiting Angle‐Independent Structural Color and Its Application to Detection of Biogenic Amines

When Machine Learning Meets 2D Materials: A Review

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