Interpretable spectrum machine learning methods for materials identification and heat transfer tubes failure detection
Authors:   , Wang Hong 
Published: December 2023

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] High-throughput synthesis and rapid characterization techniques have shown great potential in accelerating the inverse design and discovery of new materials. A large volume of characterization data such as X-ray diffraction (XRD), Raman and FTIR spectrum collected in a short time, creates significant challenges to data analysis. Among them, X-ray diffraction (XRD) is a workhorse technique to determine crystallography and phase information, including lattice parameters, crystal symmetry, phase composition, density, space group, and dimensionality. Typically, experiment XRD pattern data are analyzed by obtaining descriptors such as peak shape, height, and position. Matching descriptors of the test pattern to known XRD patterns in crystalline databases allows the identification of the compound of interest. Traditionally, it is tedious and time consuming to analyze these data individually, leading to data analysis at a relatively low speed and severely hindering the application of new data for subsequent decision making. To indeed make optimal use of the characterization resources and data collection capability, it is currently urgent to design and deploy data assessment tools with automation and recommendation ability. Recently, Machine Learning(ML) techniques have proven invaluable to manage the ever-growing volume of materials research data produced by high-throughput materials simulation, fabrication, and characterization. It has been demonstrated for their utility in rapidly and automatically identifying potential composition-phase maps. Recently, researchers reported automatic and rapid classification of XRD using new ML algorithms. Park et al. demonstrated a well-trained convolutional neural network (CNN) that exhibited a satisfactory classification of the XRD spectrum based on the theoretical database. Oviedo and his colleagues proposed a ML enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns[2]. However, these approaches assigned the XRD spectrum into seven groups, which significantly decrease the difficulties of identification problems. It is more practicable to directly identify the unknown XRD spectrum through rapidly comparing it to the theoretical database. Another issue is that even significant achievement has been obtained, the ML algorithm is still in its infancy and called as 'black-box', which means it is difficult to interpret the prediction results in a visible or scientific meaningful way. However, collaboration has been restricted by black-box ML. It is hard for scientists to understand the data-driven model. Therefore, the notion of interpretable ML (explainable or transparent), which has not only high prediction accuracy score, but high interpretability, has recently seen a resurgence, especially for scientific discoveries led by ML. Here, we propose a CNN model based on the noise-augmentation technique for rapid identification of the XRD spectrum of metal-organic frameworks. It is the first to identify the XRD spectrum instead of group classification. The well-trained CNN model showed excellent one-by-one identification accuracy (96.7 percent) for the Top 5 ranking on a test dataset of 30 hold-out samples. With the data augmentation, Deep Learning (DL), and ML methods successfully applied on XRD spectrum data, these techniques are also utilized on heat transfer tubes failure detection. Heat transfer tubes are widely used in power plants for the crucial step to energy conversion efficiency. One of the Non-destructive techniques, Eddy Current testing, is another spectrum data. With our proposed DL methods, the prediction processing can get fast and accurat