An Improved High-Throughput Data Processing Based on Combinatorial Materials Chip Approach for Rapid Construction of Fe–Cr–Ni Composition-Phase Map

The combinatorial materials chip approach is vastly superior to the conventional one that characterizes one sample at a time in the efficiency of composition-phase map construction. However, the resolution of its high-throughput characterization and the correct rate of automated composition-phase mapping are often affected by inherent experimental limitations and imperfect automated analyses, respectively. Therefore, effective data preprocessing and refined automated analysis methods are required to automatically process huge amounts of experiment data to score a higher correct rate. In this work, the pixel-by-pixel structural and compositional characterization of the Fe–Cr–Ni combinatorial materials chip annealed at 750 °C was performed by microbeam X-ray at a synchrotron light source and by electron probe microanalysis, respectively. The severe baseline drift and system noise in the X-ray diffraction patterns were successfully eliminated by the three-step automated preprocessing (baseline drift removal, noise elimination, and baseline correction) proposed, which was beneficial to the subsequent quantitative analysis of the patterns. Through the injection of human experience, hierarchy clustering analyses, based on three dissimilarity measures (the cosine, Pearson correlation coefficient, and Jenson–Shannon divergence), were further accelerated by the simplified vectorization of the preprocessed X-ray diffraction patterns. As a result, a correct rate of 91.15% was reached for the whole map built automatically in comparison with the one constructed manually, which confirmed that the present data processing could assist humans to improve and expedite the processing of X-ray diffraction patterns and was feasible for composition-phase mapping. The constructed maps were generally consistent with the corresponding isothermal section of the Fe–Cr–Ni ternary alloy system in the ASM Alloy Phase Diagram Database except the inexistence of the σ phase under insufficient annealing.

组合材料芯片（combinatorial materials chip）方法在构建成分相图（composition-phase map）的效率层面，远优于单次表征单一样本的传统实验范式。然而，其高通量表征的分辨率与自动化成分相图绘制的正确率，分别易受固有实验局限与不完善的自动化分析流程制约。因此，亟需高效的数据预处理与精细化的自动化分析方法，以自动处理海量实验数据，提升成分相图绘制的整体正确率。本研究中，针对经750℃退火处理的Fe-Cr-Ni组合材料芯片，分别采用同步辐射光源（synchrotron light source）下的微束X射线技术与电子探针显微分析（electron probe microanalysis），完成了逐像素的结构与成分表征。本研究提出的三步自动化预处理流程（基线漂移去除、噪声消除与基线校正），成功剔除了X射线衍射图谱（X-ray diffraction pattern）中严重的基线漂移与系统噪声，为后续的图谱定量分析提供了可靠支撑。通过融入人类先验经验，结合预处理后X射线衍射图谱的简化向量化操作，基于三种相异性度量（余弦、皮尔逊相关系数与詹森-香农散度）的层次聚类分析得以进一步提速。最终，与手动构建的成分相图对照，自动生成的全成分相图正确率达到91.15%。该结果证实，本研究的数据处理方法可辅助科研人员优化并加快X射线衍射图谱的处理流程，且可用于自动化成分相图绘制。所构建的成分相图整体与ASM合金相图数据库（ASM Alloy Phase Diagram Database）中Fe-Cr-Ni三元合金体系的对应等温截面高度吻合，仅因退火不充分，未观测到σ相（σ phase）的存在。