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

CDS

• 批准号: 2202124
• 负责人: Niaz Abdolrahim
• 金额: $ 37.5万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2202124&HistoricalAwards=false
• 关键词: CDS& Inferring; Lattice; Dynamics; Temporal

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.

非技术概要新兴的 X 射线散射实验技术提供了以原子尺度（约十亿分之一米）灵敏度和万亿分之一秒时间分辨率探测材料的原子结构和物理特性之间的相关性的能力。在这些散射实验中获得的图像包含视觉特征，例如环、斑点和光晕，它们编码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，无法仅由人类专家来手动分析具有许多不确定性的数据。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，以检测材料在承受高压、温度或应变等极端条件时的原子尺度相变和塑性变形机制。对控制原子排列和运动的机制有一个基本的了解对于确定在极端条件下形成具有所需特性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方面的多学科培训。该项目将为新课程材料和应用于材料科学的人工智能模块的设计提供信息。 PI 还将参与 K-12 学生的外展活动，旨在扩大科学、技术、工程和数学领域代表性不足群体的参与，并提高对纳米技术和材料科学的认识。 技术摘要该奖项支持旨在开发自动化的研究和教育活动深度学习计算机视觉技术可挖掘 X 射线衍射 (XRD) 数据，以识别晶体结构并检测极端条件下负责相变和塑性变形的晶格级机制。在非常高的压力、温度或应变率下，当事先无法得知晶格变化或新相的出现时，分析来自数十亿次 XRD 测量的大量快照数据集会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、重构、时空建模、弱监督、领域适应和可视化来实现以下目标：1）静态和时间的生成合成一维 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) 领域适应大量实验数据。这种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，更深入地理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、高级原子级结构等方面的多学科培训。 /化学表征、分子动力学模拟和深度学习技术。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 学生的外展活动，旨在扩大科学、技术、工程和数学领域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，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。