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Analysis of extended X-ray absorption fine structure (EXAFS) data using artificial intelligence techniques

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We have addressed the issue of improper and unreliable analysis of materials characterization data by developing an artificial intelligence based methodology that can reliably and more efficiently analyze experimental results from extended X-ray absorption fine structure (EXAFS) measurements. Such methods help address growing reproducibility problems that are slowing research progress, discouraging the quest for research excellence, and inhibiting effective technology transfer and manufacturing innovation. We have developed a machine learning system for automated analysis of EXAFS spectroscopy measurements and demonstrated its effectiveness on measurements collected at powerful, third generation synchrotron radiation facilities. Specifically, the system uses a genetic algorithm to efficiently find sets of structural parameters that lead to high quality fits of the experimental spectra. A human analyst suggests a set of chemical compounds potentially present in the sample, used as theoretical standards. The algorithm then searches the large multidimensional space of combinations of these materials to determine the set of structural paths using the theoretical standards that best reproduces the experimental data. The algorithm further calculates a goodness of fit value from the suggested standards that can be used to identify the chemical moieties present in the measured sample.
机译：我们通过开发了基于人工智能的方法来解决了材料表征数据的不当和不可靠分析的问题，这些方法可以可靠，更有效地分析来自延长的X射线吸收细结构（EXAFS）测量的实验结果。此类方法有助于解决正在减缓研究进展的不断重现性问题，令人抑制追求研究卓越，并抑制有效的技术转让和制造创新。我们开发了一种机器学习系统，用于自动分析EXAFS光谱测量，并在强大的第三代同步辐射设施中展示了其对收集的测量的有效性。具体地，该系统使用遗传算法能够有效地找到导致实验光谱的高质量配合的结构参数集。人体分析师表明，样品中可能存在的一组化合物，用作理论标准。然后，该算法搜索这些材料的组合的大型多维空间，以确定使用最佳再现实验数据的理论标准的结构路径集。该算法进一步计算了可用于鉴定所测量样品中存在的化学部分的建议标准的拟合值的良好。

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