DMREF: Deblurring our View of Atomic Arrangements in Complex Materials for Advanced Technologies

DMREF：模糊我们对先进技术复杂材料中原子排列的看法

• 批准号: 1534910
• 负责人: Simon Billinge
• 金额: $ 98.28万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2021-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1534910&HistoricalAwards=false
• 关键词: DMREF; Deblurring; our; View; Atomic

DMREF: Deblurring our View of Atomic Arrangements in Complex Materials for Advanced TechnologiesNon-technical Description: As we try and find new technologies to solve some of mankind's toughest challenges such as abundant sustainable energy, environmental remediation, and health, we are increasingly seeking more and more complex materials. We already have devices that turn sunlight into electricity and use sunlight to split water into precious hydrogen fuel, but issues such as device efficiency and cost mean that the current technologies cannot be taken to the vast scale needed for our modern needs. This puzzle may be solved by the use of advanced materials that perform their tasks - energy conversion, cancer cell killer, or whatever it may be - with greater efficiency. This is inevitably leading us towards more complicated materials that consist of many different chemical elements and have engineered structures on multiple different length-scales from the atomic to the nano- and meso-scales all the way to macroscopic scales. The problem is that, because of their complexity, it becomes very difficult to even characterize these materials when we have made them, let alone design and engineer them at the nanoscale. Our usual tools based on the scattering of x-rays by crystals stop working for such nanoscale structures. The problem is not that we lack powerful enough x-ray beams. The problem is that the x-ray scattering signal from these complicated materials doesn't contain enough information to allow us to find a unique structure solution. It is as if we are looking at complex patterns of atomic arrangements through blurry, steamed up glasses. This project will bring greater clarity to this situation by marrying together advances in applied mathematics from diverse areas such as image recognition, information theory and machine learning, which are having transformative impacts in commerce, law enforcement and so on, and applying them to the problem of recognizing atomic arrangements in materials of the highest complexity.Technical Description: The approach will to solve multi-scale structures of materials by marrying together the latest advances in the processing of x-ray scattering data from nanomaterials, such as atomic pair distribution function (PDF) analysis, with other sources of input information such as small angle scattering, EXAFS and other spectroscopies, as well as inputs from first principle theory such as DFT, but place them in a rigorous mathematical framework and a robust computational framework such that the information content in the data may be utilized to the greatest extent possible whilst taking into account uncertainties from statistical and systematic uncertainties. The mathematical framework will utilize the latest developments in stochastic optimization, uncertainty quantification including function-space Bayesian methods, machine learning and image recognition.

DMREF：消除我们对高级技术的复杂材料中原子布置的看法：当我们尝试寻找新技术来解决人类最艰巨的挑战，例如丰富的可持续能源，环境恢复和健康，我们越来越多地寻求越来越复杂的材料。 我们已经拥有将阳光变成电力的设备，并利用阳光将水分解为珍贵的氢燃料，但是设备效率和成本等问题意味着当前技术无法将其带入我们现代需求所需的巨大规模。可以通过使用执行其任务的高级材料（能量转换，癌细胞杀手或任何可能的东西）来解决这个难题。 这不可避免地使我们迈向了由许多不同的化学元素组成的更复杂的材料，并且已经在多个不同的长度尺度上设计了从原子到纳米和中间量表一直到宏观尺度的多个不同长度尺度的结构。 问题在于，由于它们的复杂性，甚至在我们制造它们时，这些材料的表征变得非常困难，更不用说在纳米级设计并设计它们了。 我们的通常的工具基于X射线散射的晶体散射，停止在此类纳米级结构上工作。 问题不是我们缺乏足够强大的X射线梁。 问题在于，来自这些复杂材料的X射线散射信号不包含足够的信息，使我们能够找到独特的结构解决方案。 好像我们正在通过模糊，蒸的眼镜来研究原子布置的复杂模式。 该项目将通过将图像识别，信息理论和机器学习等不同领域的应用数学的进步结合在一起，使这种情况更加清晰，这些数学在商业，执法等方面具有变革性的影响，并将其应用于最高材料中的原子安排中的原子安排，以构建材料的方法：构造材料的方法：来自纳米材料的X射线散射数据，例如原子配对分布函数（PDF）分析，以及其他输入信息，例如小角度散射，EXAF和其他光谱镜，以及第一原理理论的输入，例如DFT，例如DFT，但将其放置在严格的数学框架和强大的计算框架中，可以将其用于强大的计算框架，以实现数据的范围，以实现数据，以实现数据的范围。统计和系统不确定性的不确定性。 数学框架将利用随机优化的最新发展，不确定性量化，包括功能空间贝叶斯方法，机器学习和图像识别。