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Sequential piezoresponse force microscopy and the ‘small-data’ problem(顺序压电响应力显微镜和“小数据”问题)
发布时间:2018-07-17

Sequential piezoresponse force microscopy and the ‘small-data’ problem(顺序压电响应力显微镜和“小数据”问题) 
Harsh TrivediVladimir V. ShvartsmanMarco S. A. MedeirosRobert C. Pullar & Doru C. Lupascu
npj Computational Materials 4:28 (2018)
doi:s41524-018-0084-9
Published online:21 june 2018
Abstract| Full Text | PDF OPEN

摘要:“大数据”对于材料领域来说不仅意味着数据集体量庞大,也指代其种类繁杂。这是在材料学及化学中进行组合搜索时遇到的常见问题。然而,由于测量中变量的步长总是受限的,因此这些数据集只能算是“小”规模的。这种限制导致了高阶统计方法的失效,而无监督学习方法的选择也只能局限于那些基于利用低阶统计的方法。作为一个有趣的案例,本研究利用可变磁场压电响应力显微镜(PFM)研究了多铁复合材料,其中压电响应的磁场依赖性,因实验限制只能得到粗步长测量数据。从该数据中有效地提取出上述与局域磁电效应相关的磁场依赖性是本研究要解决的核心问题。通过利用密度聚类方法在数据中预先标记出可能的模式,我们评估了主成分分析(PCA)作为一种简单的非监督学习技术的表现。以这种组合分析为基础,我们着重研究了在上述案例中,如何使用基于非中心二次矩的PCA,才能提取出有关局域材料响应及其空间分布的信息   

Abstract:The term big-data in the context of materials science not only stands for the volume, but also for the heterogeneous nature of the characterization data-sets.This is a common problem in combinatorial searches in materials science, as well as chemistry.However, these data-sets may well be ‘small’ in terms of limited step-size of the measurement variables.Due to this limitation, application of higher-order statistics is not effective, and the choice of a suitable unsupervised learning method is restricted to those utilizing lower-order statistics. As an interesting case study, we present here variable magnetic-field Piezoresponse Force Microscopy (PFM) study of composite multiferroics, where due to experimental limitations the magnetic field dependence of piezoresponse is registered with a coarse step-size. An efficient extraction of this dependence, which corresponds to the local magnetoelectric effect, forms the central problem of this work.We evaluate the performance of Principal Component Analysis (PCA) as a simple unsupervised learning technique, by pre-labeling possible patterns in the data using Density Based Clustering (DBSCAN).Based on this combinational analysis, we highlight how PCA using non-central second-moment can be useful in such cases for extracting information about the local material response and the corresponding spatial distribution. 

Editorial Summary

Scanning probe microscopy: machine-assisted material insights(扫描探针显微镜:机器学习辅助材料探微) 

无监督机器学习方法可以从小型且有噪声的数据集中识别材料响应的重要特征。扫描探针显微镜的功能成像模式,可以显示材料性能随外场作用(如温度或磁场)变化反映材料性质的变化。然而,由于自由度数目很多,表征全参数空间所需的测量次数十分巨大。来自德国杜伊斯堡-埃森大学和葡萄牙阿威罗大学的Harsh Trivedi及其同事证明,数据科学技术能够从较少的测量结果中提取重要规律。他们发现,基于密度聚类和主成分分析算法所得到的关键特征,成功地捕获了两种材料之间的磁电响应差异。该技术可以更广泛地应用于功能材料的分析,既方便又便宜。

Unsupervised learning methods can identify the important features of a material’s response from small and noisy datasets. Functional imaging modes for scanning probe microscopy map the changes in material properties in response to external factors such as temperature or magnetic fields.However, the large number of degrees of freedom means the number of measurements required to characterize the full parameter space can be prohibitively high.Harsh Trivedi and co-workers from the University of Duisburg-Essen, Germany and the Univerity of Aveiro, Portugal have demonstrated that data science techniques are able to extract insights from fewer measurements.They found that the key features identified by density-based clustering and principal component analysis algorithms successfully captured the difference in magnetoelectric response between two materials.Broader application of these techniques could reduce the cost and difficulty of functional materials analysis.

 

 
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