PDF(Pubmed)
Abstract：
Mineral identification and quantification are key to the understanding and, hence, the capacity to predict material properties. The method of choice for mineral quantification is powder X-ray diffraction (XRD), generally using a Rietveld refinement approach. However, a successful Rietveld refinement requires preliminary identification of the phases that make up the sample. This is generally carried out manually, and this task becomes extremely long or virtually impossible in the case of very large datasets such as those from synchrotron X-ray diffraction computed tomography. To circumvent this issue, this article proposes a novel neural network (NN) method for automating phase identification and quantification. An XRD pattern calculation code was used to generate large datasets of synthetic data that are used to train the NN. This approach offers significant advantages, including the ability to construct databases with a substantial number of XRD patterns and the introduction of extensive variability into these patterns. To enhance the performance of the NN, a specifically designed loss function for proportion inference was employed during the training process, offering improved efficiency and stability compared with traditional functions. The NN, trained exclusively with synthetic data, proved its ability to identify and quantify mineral phases on synthetic and real XRD patterns. Trained NN errors were equal to 0.5% for phase quantification on the synthetic test set, and 6% on the experimental data, in a system containing four phases of contrasting crystal structures (calcite, gibbsite, dolomite and hematite). The proposed method is freely available on GitHub and allows for major advances since it can be applied to any dataset, regardless of the mineral phases present.
摘要：
矿物识别和量化是理解和理解的关键，因此，预测材料特性的能力。矿物定量的选择方法是粉末X射线衍射(XRD)，通常使用Rietveld细化方法。然而,成功的Rietveld改进需要对组成样品的阶段进行初步鉴定。这通常是手动进行的，在诸如同步加速器X射线衍射计算机断层扫描之类的非常大的数据集的情况下，这项任务变得非常长或几乎不可能。为了避免这个问题，本文提出了一种新的神经网络（NN）方法，用于自动化相位识别和量化。XRD图案计算代码用于生成用于训练NN的合成数据的大数据集。这种方法提供了显著的优势，包括构建具有大量XRD图案的数据库的能力，以及在这些图案中引入广泛的可变性。为了提高NN的性能，在训练过程中采用了专门设计的用于比例推断的损失函数，与传统功能相比，提供更高的效率和稳定性。NN,专门用合成数据训练，证明了其在合成和真实的XRD图案上识别和量化矿物相的能力。在合成测试集上进行相位量化时，训练的NN误差等于0.5%，和6%的实验数据，在包含四个相反晶体结构相的系统中(方解石，Gibbsite,白云石和赤铁矿)。所提出的方法可在GitHub上免费获得，并允许重大进展，因为它可以应用于任何数据集。无论矿物相的存在。
