RUI: Image-Based Strong-Field Adaptive Control of Molecular Dynamics

RUI：基于图像的分子动力学强场自适应控制

• 批准号: 1404185
• 负责人: Eric Wells
• 金额: $ 12.94万
• 依托单位: Augustana University Association
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1404185&HistoricalAwards=false
• 关键词: RUI; Image; Based; Strong; Field

Non-technical description:Traditional chemical synthesis methods resemble cooking in the sense that the ingredients, mixing methods, temperature, and pressure all may be varied to produce a desired result. Despite control over all of these macroscopic variables there are some chemical processes that remain elusive. Since the invention of the laser in the 1960s, the field of coherent control has sought to use the laser to manipulate chemical dynamics by applying energy of suitable color and duration directly to individual molecules. In this sense, the laser can be thought of as a new type of reagent that drives a chemical reaction. While easily stated, this task has proved challenging. Molecules are complicated and dynamic, making it difficult to determine the correct laser characteristics to drive a particular process. A proven method for approaching this problem is to use experimental feedback to guide an adaptive search of the possible laser pulses. In a physics version of natural selection, laser pulses that provide a better outcome are given an increased chance to survive and have their characteristics contribute to the tailored pulse that ultimately produces the desired outcome. Such a method, however, is only as good as the feedback that drives it. The goal of these studies is to develop enhanced image-based feedback techniques that enable this adaptive approach to coherent control of chemical dynamics. Technical description:The PI has recently developed the ability to use three-dimensional momentum imaging of the laser-molecule reaction products to define the control objective described in the non-technical description above. Using three-dimensional imaging to target a specific final state has led to better understanding of the mechanisms that undergird the laser-based control, especially in situations in which obtaining precise optical spectroscopic feedback is impractical. Increased mechanistic understanding can subsequently lead to better search parameterization, enhancing the adaptive control process. Current efforts are focused on applying image-based adaptive control to study and influence photoisomerization processes in small molecules such as ethylene. By studying photoionization, it is possible to probe how electronic excitation is rapidly converted to nuclear motion, an essential step in many ultrafast chemical processes. Ethylene is particularly interesting as a benchmark molecule for examining the role of conical intersections in these electronic to nuclear energy conversions. These studies will be advanced by using two-pulse experiments, which allow the separation of the ionization step from the subsequent evolution of the molecular ion, which the group hopes to control. Strong-field tunneling ionization of polyatomic molecules often involves multiple molecular orbitals, and studies of these relationships help link the image-based feedback to more specific target states, again with the objective of refining adaptive control methods. A goal of this project will be extending this work to explore the control of laser driven electron rescattering via feedback derived from angle resolved photoelectron distributions. Electron rescattering is an essential part of many ultrasfast laser-based processes, such as high-harmonic generation and the production of attosecond pulses, and therefore control of this sort has a number of potential applications. Finally, as an undergraduate institution, this project helps identify and develop talented students by immersing them in forefront research.

非技术描述：传统的化学合成方法类似于烹饪，从某种意义上说，成分，混合方法，温度和压力可能会变化以产生预期的结果。 尽管控制了所有这些宏观变量，但仍然难以捉摸的某些化学过程。 自1960年代激光发明以来，相干控制领域一直试图使用激光来通过将合适的颜色和持续时间直接应用于单个分子的能量来操纵化学动力学。 从这个意义上讲，激光可以被认为是一种驱动化学反应的新型试剂。 尽管很容易说明，但这项任务已被证明具有挑战性。 分子是复杂且动态的，因此很难确定正确的激光特性来驱动特定过程。 解决此问题的一种经过验证的方法是使用实​​验反馈来指导对可能的激光脉冲的自适应搜索。 在物理选择的自然选择中，提供更好结果的激光脉冲可以增加生存的机会，并具有最终产生预期结果的量身定制的脉冲。 但是，这种方法仅与驱动它的反馈一样好。 这些研究的目的是开发增强的基于图像的反馈技术，从而使这种适应性方法能够连贯地控制化学动力学。 技术描述：PI最近开发了使用激光分子反应产物的三维动量成像来定义上面非技术描述中描述的控制目标的能力。 使用三维成像来瞄准特定的最终状态，已经使人们更好地了解了基于激光的控制的机制，尤其是在获得精确的光学光谱反馈的情况下，不切实际。 增加的机械理解随后会导致更好的搜索参数化，从而增强自适应控制过程。当前的努力专注于应用基于图像的自适应控制来研究和影响小分子（例如乙烯）中的光异构化过程。通过研究光子化，可以探测电子激发如何迅速转化为核运动，这是许多超快化学过程的重要步骤。 作为基准分子，乙烯特别有趣，用于检查锥形交叉点在这些电子到核能转化中的作用。 这些研究将通过使用两个脉冲实验进行进行，这允许将电离步骤从分子离子的随后演变中分离出来，该小组希望对其进行控制。 多原子分子的强场隧穿电离通常涉及多个分子轨道，对这些关系的研究有助于将基于图像的反馈与更特定的目标状态联系起来，以再次提高适应性控制方法。 该项目的一个目标是扩展这项工作，以通过从角度分辨的光电子分布中得出的反馈来探索激光驱动的电子脱落的控制。 电子拆卸是许多基于超快速激光的过程的重要组成部分，例如高谐波产生和Attosecond脉冲的产生，因此控制这种类型的脉冲具有许多潜在的应用。 最后，作为一家本科机构，该项目通过将他们浸入前列研究中来帮助识别和发展有才华的学生。