- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Materials-Informatics-Assisted High-Yield Synthesis of 2D Nanomaterials through Exfoliation
摘要： A variety of inorganic and organic nanosheets with characteristic structures and properties can be synthesized through exfoliation of layered materials. However, in general, immense time and efforts are required for exploration of exfoliation conditions and characterization of nanosheets. In addition, it is challenging to improve the yield of nanosheets obtained through exfoliation. Here a materials-informatics-assisted high-yield synthesis of nanosheets is proposed, which does not require experience and intuition. Layered composites containing inorganic layers and interlayer organic guests are delaminated into nanosheets in a variety of dispersion media. First, an experimental screening is performed to find efficient exfoliation conditions and obtain a training dataset for the informatics approach. Sparse modeling is then used facilitating the extraction of important factors predicting the yield of nanosheets. High-yield (up to (cid:2)50%) synthesis of nanosheets is demonstrated in unknown systems in a minimum number of experiments. The yield is higher than those typically reported for layered materials. It is expected that the effective combination has potentials for not only discovery of compounds but also structure control of materials.
关键词： sparse modeling,layered materials,exfoliation,2D nanomaterials,materials informatics
更新于2025-09-23 15:22:29
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Exploring materials band structure space with unsupervised machine learning
摘要： An unsupervised machine learning algorithm is applied for the first time to explore the space of materials electronic band structures. T-student stochastic neighbor embedding (t-SNE), a state of the art algorithm for visualization of high dimensional data, is applied on feature spaces constructed by extracting electronic fingerprints straight from Brillouin zone of the materials. Different spaces are designed and mapped to lower dimensions allowing to analyze and explore this previously uncharted band structure space for thousands of materials at once. In all cases analyzed machine learning was able to learn and cluster the materials depending on the features involved. t-SNE promises to be a extremely useful tool for exploring the materials space.
关键词： Fermiology,Data visualization,Band structure,Unsupervised machine learning,Data mining,Materials informatics,High throughput materials calculations
更新于2025-09-11 14:15:04