CDS&E: Inferring Lattice Dynamics from Temporal X-ray Diffraction Data

非技术性总结新兴的X射线散射实验技术提供了探测原子结构和材料物理性质之间相关性的能力，具有原子尺度（约十亿分之一米）的灵敏度和万亿分之一秒的时间分辨率。在这些散射实验中获得的图像包含视觉特征，如环，斑点和晕，它们编码了有关原子结构及其时间演化的详细信息。然而，相应的数据集非常大，因此，手动分析它们具有许多不确定性，不能仅由人类专家执行。该奖项支持研究和教育活动，以开发人工智能技术，从X射线散射实验中挖掘数据，以检测材料在高压，温度或应变等极端条件下的相变和塑性变形的原子级机制。实现对原子排列和运动机制的基本理解对于确定在极端条件下形成具有所需性质和行为的新物质的新途径至关重要。该项目还将为本科生和研究生提供计算材料科学，高级原子级结构/化学表征，分子动力学模拟和人工智能技术的多学科培训。该项目将为新课程材料和人工智能应用于材料科学模块的设计提供信息。PI还将参与K-12学生的外联活动，旨在扩大代表性不足的群体在科学，技术，工程，该奖项支持旨在开发自动化深度学习计算机视觉技术的研究和教育活动，以挖掘X射线衍射（XRD）数据，从而识别晶体结构并检测晶格。在极端条件下负责相变和塑性变形的水平机制。在非常高的压力、温度或应变速率下，当晶格变化或新相的出现事先未知时，分析来自数十亿XRD测量的大量快照数据集变得不准确，或完全失败。为了克服这一挑战，研究团队将利用一系列新颖和先进的技术，包括多模态融合，重建，时空建模，弱监督，域适应和可视化，以实现以下目标：1）生成静态和时间合成一维XRD图案和二维XRD图像，2）开发用于晶体结构静态和时间分类的深度学习模型，3）开发用于解释和证明深度学习模型和预测的解释技术，以及4）对大型实验数据的领域适应。该深度学习技术的成功开发将有助于在没有先验知识的情况下更深入地了解极端条件下材料中的未知现象，该项目还将为本科生和研究生提供计算材料科学，高级原子级结构/化学表征，分子动力学模拟和深度学习技术的多学科培训。该项目将为材料科学应用深度学习的新课程材料和模块的设计提供信息。PI还将参与K-12学生的外联活动，旨在扩大科学，技术，工程和数学方面代表性不足的群体的参与，并提高对纳米技术和材料科学的认识。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

NONTECHNICAL SUMMARYEmerging X-ray scattering experimental techniques provide the capability to probe correlations between atomic structure and physical properties of materials with atomic-scale (around one-billionth of a meter) sensitivity and with one trillionth of a second time resolution. The images obtained in these scattering experiments contain visual features, such as rings, spots, and halos, which encode detailed information about the atomic structure and its time evolution. However, the corresponding data sets are enormously large, and therefore, manually analyzing them with many uncertainties cannot be solely performed by human experts. This award supports research and educational activities to develop artificial intelligence techniques to mine data from X-ray scattering experiments to detect atomic-scale mechanisms of phase transformations and plastic deformation in materials when they are subjected to extreme conditions like high pressure, temperature or strain. Achieving a fundamental understanding of the mechanisms that govern the arrangement and motion of atoms is crucial for identifying new pathways of forming new matter with desired properties and behavior at extreme conditions. This project will also provide multidisciplinary training for undergraduate and graduate students in computational materials science, advanced atomic-level structural/chemical characterization, molecular dynamics simulations, and artificial intelligence techniques. The project will inform the design of new course material and modules on artificial intelligence applied to materials science. The PIs will also be involved in outreach to K-12 students aimed at broadening participation of underrepresented groups in science, technology, engineering, and mathematics and raise awareness of nanotechnology and materials science.TECHNICAL SUMMARYThis award supports research and educational activities aimed at developing automated deep-learning computer vision techniques to mine x-ray diffraction (XRD) data to identify crystal structures and detect lattice-level mechanisms responsible for phase transformation and plastic deformation under extreme conditions. At very high pressures, temperatures, or strain rates when lattice variations or occurrence of new phases are not known a priori, analyzing vast datasets of snapshots from billions of XRD measurements become inaccurate, or fail completely. To overcome this challenge, the research team will leverage a series of novel and advanced techniques, including multimodal fusion, reconstruction, space-time modeling, weak supervision, domain adaptation, and visualization to achieve the following objectives: 1) Generation of static and temporal synthetic one-dimensional XRD patterns and two-dimensional XRD images, 2) Development of deep learning models for static and temporal classification of crystal structures, 3) Development of interpretation techniques for explanation and justification of deep learning models and predictions, and 4) Domain adaptation to large experimental data. The successful development of such deep learning techniques will lead to deeper understanding of unknown phenomena in materials under extreme conditions when no prior knowledge is available.This project will also provide multidisciplinary training for undergraduate and graduate students in computational materials science, advanced atomic-level structural/chemical characterization, molecular dynamics simulations, and deep learning techniques. The project will inform the design of new course material and modules on applied deep learning for materials science. The PIs will also be involved in outreach to K-12 students aimed at broadening participation of underrepresented groups in science, technology, engineering, and mathematics and raise awareness of nanotechnology and materials science.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.