X射線衍射技術是一種常用的材料學特征分析方法，對于材料的相態鑒定十分重要。但是，傳統的X射線衍射技術需要手動選取參數并進行掃描，效率較低，而且一些材料可能會因為相變或者形變而改變衍射譜，造成誤判。因此，研究如何使得X射線衍射技術更智能化、高效化，對于提高材料相態鑒定的精度和速度具有重要意義。
Fig. 1 A schematic for adaptively driven XRD with autonomous phase identification
由美國加州大學伯克利分校材料科學與工程系的Gerbrand Ceder教授（本刊編委）領導的團隊，在基于卷積神經網絡的ML算法的驅動下，制定了一種用于自主相位識別的自適應引導XRD技術。不確定性量化用于決定何時需要額外的測量，而類激活映射分析則表明在何處執行這些測量。
Fig. 2 F1-scores achieved by XRD-AutoAnalyzer when applied to simulated patterns in the (top) Li-La-Zr-O and (bottom) Li-Ti-P-O spaces.
基于Li-La-Zr-O和Li-Ti-P-O化學空間的材料，該方法在三個復雜程度不斷增加的測試案例中得到了驗證。這些測試表明，自適應XRD在模擬和實驗獲得的圖像上始終優于傳統方法，并可以提供更精確的雜質相檢測，同時測量時間更短。作者進一步證明，該ML方法可以有效地指導XRD測量，以Li7La3Zr2O12（LLZO）的合成為例，改善固相反應的原位表征。使用自適應掃描來監測LLZO的合成，成功地識別了一個短壽命的中間相，而傳統測量通常漏掉了這種中間相的表征。
Fig. 3 Detection rates and measurement times required for impurity detection
這些發現為動態過程的自適應表征提供了一個清晰的概念證明，突出了由ML驅動的自主實驗的機會。該文近期發表于npj Computational Materials??9: 31 (2022).
Fig. 4 In situ identification of phases formed during LLZO synthesis.
A?novel and efficient method for phase detection of materials
X-ray diffraction technique is a commonly used material science characterization method, which is important for phase identification of materials. However, the traditional X-ray diffraction technique requires manual selection of parameters and scanning, which is inefficient, and some materials may change the diffraction spectrum due to phase change or deformation, resulting in misclassification. Therefore, it is important to study how to make the X-ray diffraction technique more intelligent and efficient to improve the accuracy and speed of material phase identification.?
A team lead by Prof. Gerbrand Ceder from Department of Materials Science & Engineering, UC Berkeley, USA, formulated an adaptively steered XRD technique for autonomous phase identification, driven by an ML algorithm based on a convolutional neural network. Uncertainty quantification is used to decide when additional measurements are needed, while class activation map analysis dictates where those measurements are performed. This approach is validated it on three test cases with increasing complexity based on materials from the Li-La-Zr-O and Li-Ti-P-O chemical spaces. These tests reveal that adaptive XRD consistently outperforms conventional methods on both simulated and experimentally acquired patterns, providing more precise detection of impurity phases while requiring shorter measurement times. The authors further demonstrate that the ML approach can effectively guide XRD measurements for improved in situ characterization of solid-state reactions, with the synthesis of Li7La3Zr2O12 (LLZO) considered as an example. The use of adaptive scans to monitor LLZO synthesis led to the successful identification of a short-lived intermediate phase that would otherwise be missed by conventional measurements.?
These findings provide a clear proof of concept for adaptive characterization of dynamic processes, highlighting the opportunity for autonomous experiments driven by ML.?This article was recently published in npj Computational Materials 9: 31 (2022).
Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification (由機器學習指導的自適應驅動X射線衍射的自主相位識別)
Nathan J. Szymanski,?Christopher J. Bartel,?Yan Zeng,?Mouhamad Diallo,?Haegyeom Kim?&?Gerbrand Ceder?
Abstract?Machine learning (ML) has become a valuable tool to assist and improve materials characterization, enabling automated interpretation of experimental results with techniques such as X-ray diffraction (XRD) and electron microscopy. Because ML models are fast once trained, there is a key opportunity to bring interpretation in-line with experiments and make on-the-fly decisions to achieve optimal measurement effectiveness, which creates broad opportunities for rapid learning and information extraction from experiments. Here, we demonstrate such a capability with the development of autonomous and adaptive XRD. By coupling an ML algorithm with a physical diffractometer, this method integrates diffraction and analysis such that early experimental information is leveraged to steer measurements toward features that improve the confidence of a model trained to identify crystalline phases. We validate the effectiveness of an adaptive approach by showing that ML-driven XRD can accurately detect trace amounts of materials in multi-phase mixtures with short measurement times. The improved speed of phase detection also enables in situ identification of short-lived intermediate phases formed during solid-state reactions using a standard in-house diffractometer. Our findings showcase the advantages of in-line ML for materials characterization and point to the possibility of more general approaches for adaptive experimentation.
摘要 機器學習（ML）已成為協助和改善材料表征的寶貴工具，能夠通過X射線衍射(XRD)和電子顯微鏡等技術自動解釋實驗結果。由于ML模型一旦訓練好就會很快，因此有一個關鍵機會可將解釋與實驗結合起來，并即時做出決策以實現最佳測量效果，這為快速學習和從實驗中提取信息創造了廣泛的機會。在這里，我們通過開發自主和自適應XRD來證明這種能力。通過將ML算法與物理衍射儀耦合，該方法集成了衍射和分析過程，以便利用早期的實驗信息來引導測量，最終提高訓練以識別晶體相模型的置信度。我們通過證明ML驅動的XRD可以在較短的測量時間內準確檢測出多相混合物中的微量材料，驗證了自適應方法的有效性。相檢測速度的提高也使我們能夠使用標準的內部衍射儀在固態反應中形成的短壽命中間相進行原位識別。我們的研究結果展示了內聯ML在材料表征方面的優勢，并指出了更通用的自適應實驗方法的可能性。
原創文章，作者：計算搬磚工程師，如若轉載，請注明來源華算科技，注明出處：http://www.zzhhcy.com/index.php/2024/03/18/7ef37edd99/
