npj: 智能机器学习—通过维堆叠揭示物理和化学的贡献

来源：知社学术圈

降维和机器学习方法可以快速建立材料物理化学属性与其性能间的内在关系，是当前材料科学研究中的重要突破。然而由于在分析中不能施加物理或化学约束，这些方法的使用受到固有的限制。

来自美国佐治亚理工学院材料科学与工程学院的Nazanin Bassiri-Gharb领导的团队，巧妙地使用维度堆叠方法可以从多维数据集中提取出有意义的化学和物理特征。在传统的数据降维前采用维度堆叠将物理和化学约束引入分析中，可以提供更为准确的材料属性参数与性能间的关系。为证明该方法的有效性，作者将其应用到PMN-PT弛豫铁电体机电响应的纳米尺度研究中。结果表明，相比简单的统计分析和传统降维方法，仅有该方法可以在全相图空间中准确地定量描述不同物理属性参数对于机电响应的贡献。本研究提出的维度叠加方法，可广泛用于复杂组分的功能材料的响应分析，以及新功能材料的设计。

该文近期发表于npj Computational Materials 5: 85 (2019)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Smart machine learning or discovering meaningful physical and chemical contributions through dimensional stacking 

Lee A. Griffin, Iaroslav Gaponenko, Shujun Zhang & Nazanin Bassiri-Gharb 

Despite remarkable advances in characterization techniques of functional materials yielding an ever growing amount of data, the interplay between the physical and chemical phenomena underpinning materials’ functionalities is still often poorly understood. Dimensional reduction techniques have been used to tackle the challenge of understanding materials’ behavior, leveraging the very large amount of data available. Here, we present a method for applying physical and chemical constraints to dimensional reduction analysis, through dimensional stacking. Compared to traditional, uncorrelated techniques, this approach enables a direct and simultaneous assessment of behaviors across all measurement parameters, through stacking of data along specific dimensions as required by physical or chemical correlations. The proposed method is applied to the nanoscale electromechanical relaxation response in (1 − x)PMN-xPT solid solutions, enabling a direct comparison of electric field- and chemical composition-dependent contributors. A poling-like, and a relaxation-like behavior with a domain glass state are identified, and their evolution is tracked across the phase diagram. The proposed dimensional stacking technique, guided by the knowledge of the underlying physics of correlated systems, is valid for the analysis of any multidimensional dataset, opening a spectrum of possibilities for multidisciplinary use.