Engineering topics

Liu, Wenjun

Publications and source records attributed to Liu, Wenjun.

3 records

DOE OSTI2025

Informed unsupervised machine learning analysis of dislocation microstructure from high-resolution differential aperture X-ray structural microscopy data

This study leverages high-resolution differential-aperture X-ray structural microscopy (DAXM) to probe the local dislocation structure in deformed 304L-stainless steel at small strain, by measuring the lattice rotation and deviatoric elastic strain with a sub-micron resolution. For a single grain in a polycrystalline specimen, the measured lattice rotation field over the measured volume exhibited a multimodal distribution while the deviatoric elastic strain showed a single-mode distribution. An unsupervised Cauchy mixture machine learning model was developed to resolve the multimodal distribution of the lattice rotation. By mapping the lattice rotation data associated with each Cauchy peak in the model back onto the measured volume, we identify contiguous regions of the crystal rotated near the average values corresponding to the peaks of the overall rotation distribution. These regions represent the grain subdivision in the microstructure. Finally, the dislocation density tensor was also computed and its norm was laid over the rotation field to detect the subgrain boundaries. This step provided a validation of the Cauchy mixture model for the analysis of the lattice rotation distribution. The current study highlights the integration of advanced X-ray microscopy techniques with data-driven analysis methods to uncover detailed microstructure scales in deformed crystals.

Machine learning; Lattice rotation; High-energy X-↗

DOE OSTI2024

Long-term relaxation of orientational disorder and structural modifications in molecular nitrogen at high pressure

Up to 17 GPa, the crystalline phases of N 2 are characterized by pronounced orientational disorder, whereas the higher-pressure phases of molecular N 2 are ordered. This raises the question about long-term relaxation of orientational disorder within the low- to intermediate-pressure regime. Here, in this study, this question is addressed by comparing synthetic with natural, chemically pure, solid N 2 that resides as inclusions in diamonds at 300 K for about 10 8 years at pressures up to 11 GPa. It is shown that disorder prevails at 8.7 GPa, 300 K, where both synthetic and natural N 2 assume the same structure. However, at 10.8 GPa, natural solid N 2 exhibits monoclinic distortion and partial orientational ordering of the molecules, both of which are not observed in synthetic material. This difference is interpreted as the result of long-term structural relaxation. The ordering mechanism is examined and placed into the context of the δ- to ε-N 2 transition. We present explanations for the absence of complete ordering of δ-N 2 .

Tschauner, Oliver↗

DOE OSTI2024

Accurate and Data‐Efficient Micro X‐ray Diffraction Phase Identification Using Multitask Learning: Application to Hydrothermal Fluids

Traditional analysis of highly distorted micro X‐ray diffraction (μ‐XRD) patterns from hydrothermal fluid environments is a time‐consuming process, often requiring substantial data preprocessing and labeled experimental data. Herein, the potential of deep learning with a multitask learning (MTL) architecture to overcome these limitations is demonstrated. MTL models are trained to identify phase information in μ‐XRD patterns, minimizing the need for labeled experimental data and masking preprocessing steps. Notably, MTL models show superior accuracy compared to binary classification convolutional neural networks. Additionally, introducing a tailored cross‐entropy loss function improves MTL model performance. Most significantly, MTL models tuned to analyze raw and unmasked XRD patterns achieve close performance to models analyzing preprocessed data, with minimal accuracy differences. This work indicates that advanced deep learning architectures like MTL can automate arduous data handling tasks, streamline the analysis of distorted XRD patterns, and reduce the reliance on labor‐intensive experimental datasets.

97 MATHEMATICS AND COMPUTING↗