AI-enhanced X-ray diffraction analysis: towards real-time mineral phase identification and quantification

IF 2.9 2区材料科学 Q2 CHEMISTRY, MULTIDISCIPLINARY

IUCrJ Pub Date : 2024-09-01 DOI:10.1107/S2052252524008157

Nikolaos I. Prasianakis

引用次数: 0

Abstract

The use of convolutional neural networks can revolutionize XRD analysis by significantly reducing processing times. Demonstration against synthetic and real mineral mixture data provide a first assessment of the accuracy of such methods.

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人工智能增强 X 射线衍射分析：实现矿物相的实时识别和量化。

使用卷积神经网络可大幅缩短处理时间，从而彻底改变 XRD 分析方法。通过对合成和真实矿物混合物数据的演示，可对此类方法的准确性进行初步评估。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IUCrJ CHEMISTRY, MULTIDISCIPLINARYCRYSTALLOGRAPH-CRYSTALLOGRAPHY

CiteScore

7.50

自引率

5.10%

发文量

审稿时长

10 weeks

期刊介绍： IUCrJ is a new fully open-access peer-reviewed journal from the International Union of Crystallography (IUCr). The journal will publish high-profile articles on all aspects of the sciences and technologies supported by the IUCr via its commissions, including emerging fields where structural results underpin the science reported in the article. Our aim is to make IUCrJ the natural home for high-quality structural science results. Chemists, biologists, physicists and material scientists will be actively encouraged to report their structural studies in IUCrJ.