课题基金 / 基金详情

EAGER: CDS&E: An Open-Source Software Package for Assessing and Controlling Photocatalytic Reactions

EAGER: CDS&E: An Open-Source Software Package for Assessing and Controlling Photocatalytic Reactions
渴望:CDS
批准号:
1833218
负责人:
Bryan Wong
金额:
$20.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-09-01 至 2020-08-31

项目摘要

项目成果

Bryan Wong的其他基金

相关文献

中文摘要
翻译
光催化是一种捕捉阳光并利用其为催化材料加速的化学反应提供能量的过程。良好的光催化体系既能有效地吸收光,又能有效地利用光产生的电子促进催化反应。光与化学/材料系统之间的相互作用使许多光催化应用成为可能,所有这些都有助于可再生能源的应用,可用于太阳能燃料发电,环境修复和化学制造等各种应用。随着我们对这些系统中发生的光引发过程的进一步了解,充分利用这些光催化系统的能力具有巨大的潜力。光催化过程的物理和化学基础是极其复杂的。近年来,通过使用预测量子力学技术来计算和理解光催化过程的效率,计算技术的发展有助于识别新的光收集和光催化材料的试错方法。该项目将进一步推进这些技术,并为一般催化社区提供一个新的开源软件包,以评估光催化过程的效率。完全开源代码和计算能力的可用性鼓励全世界的学生和研究人员更深入地了解如何使用这些方法和工具来评估光催化效率,并加速发现新的光催化材料和操作条件。本项目将利用激发态量子计算方法来探测光催化系统中的电子动力学。本项目中使用的激发态计算方法将与量子控制算法相结合,最终操纵光诱导反应动力学。通过提供一种控制反应动力学的明确方法,在光催化系统中使用量子控制方法将在多个化学工程领域具有开创性的意义。具体地说,这些计算技术将给出导致所需反应产物的光的波长/频率的严格界限。因此,在这个EAGER项目中开发的软件工具可以作为(1)一个诊断工具来验证光的正确频率是否确实在光催化实验中被预期使用,以及(2)一个预测工具来计算控制光催化系统所需的光的允许频率(可以从计算或实验中获得数据集)。因此,这些计算方法为交叉研究开辟了新的途径,为操纵电子动力学和理解光催化系统中可能的最佳效率建立了严格的形式。最后,该项目的广泛影响将产生一套开源工具,CBET和催化社区的计算和实验研究人员都可以轻松地用于未来的开发。在这个项目中开发的完全开源代码的可用性鼓励全世界的研究人员获得详细的“引擎盖下的外观”,以更深入地了解这些算法是如何在实践中应用的。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
Photocatalysis is the process by which sunlight is captured and used to provide energy for chemical reactions accelerated by catalytic materials. A good photocatalytic system is one which absorbs light efficiently, and also efficiently uses the photo-generated electrons to promote catalytic reactions. The interaction between light and chemical/material systems enables numerous photocatalytic applications, all of which contribute to renewable energy that can be used for applications as diverse as solar fuel generation, environmental remediation, and chemical manufacturing. The capability to fully harness these photocatalytic systems has tremendous potential to grow as we further our understanding of the light-initiated processes that occur in these systems. The physics and chemistry underlying photocatalytic processes are extremely complicated. Trial-and-error approaches to identifying new light-harvesting and photocatalytic materials have been aided in recent years by the development of computational techniques to calculate and understand the efficiency of photocatalytic processes using predictive quantum mechanical techniques. This project will further advance those techniques and provide a new, open-source software package for the general catalysis community to assess the efficiency of photocatalytic processes. The availability of fully open-source codes and computational capability encourages both students and researchers worldwide to obtain a deeper understanding of how these approaches and tools can be used to assess photocatalytic efficiencies and hasten the discovery of new photocatalytic materials and operating conditions.This project will utilize excited-state quantum computational methods to probe the electron dynamics in photocatalytic systems. The excited-state computational approaches used in this project will be coupled with quantum control algorithms to ultimately manipulate photo-induced reaction dynamics. The use of quantum control approaches in photocatalytic systems will have ground-breaking implications across multiple chemical engineering domains by providing a defined way to control reaction dynamics. Specifically, these computational techniques will give rigorous bounds on the wavelengths/frequencies of light that will lead to the desired reaction products. As such, the software tool developed in this EAGER project can serve as both (1) a diagnostic tool to verify that the correct frequency of light is indeed being used as intended in a photocatalysis experiment, as well as (2) a predictive tool for calculating the allowed frequencies of light required to control photocatalytic systems (which may be obtained as data sets obtained from calculation or experiment). Consequently, these computational methods open new avenues of cross-cutting research by establishing a rigorous formalism for manipulating the electron dynamics and understanding the optimal efficiencies that are possible in photocatalytic systems. Finally, the broader impacts of this project will result in an open-source suite of tools that both computational and experimental researchers in the CBET and catalysis communities can easily use for future development. The availability of the fully open-source codes developed in this project encourages researchers worldwide to get a detailed "look under the hood" to obtain a deeper understanding of how these algorithms are numerically incorporated in practice.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.
期刊论文(2)
专著(0)
科研奖励(0)
会议论文
NIC-CAGE: An open-source software package for predicting optimal control fields in photo-excited chemical systems
NIC-CAGE:用于预测光激发化学系统中最佳控制场的开源软件包
DOI: 10.1016/j.cpc.2020.107541
发表时间: 2021
期刊: Computer Physics Communications
影响因子: 6.3
作者: [Raza, Akber, Hong, Chengkuan, Wang, Xian, Kumar, Anshuman, Shelton, Christian R., Wong, Bryan M.]
通讯作者: Wong, Bryan M.
Harnessing deep neural networks to solve inverse problems in quantum dynamics: machine-learned predictions of time-dependent optimal control fields
利用深度神经网络解决量子动力学中的逆问题:依赖时间的最优控制场的机器学习预测
DOI: 10.1039/d0cp03694c
发表时间: 2020
期刊: Physical Chemistry Chemical Physics
影响因子: 3.3
作者: [Wang, Xian, Kumar, Anshuman, Shelton, Christian R., Wong, Bryan M.]
通讯作者: Wong, Bryan M.
Collaborative Research: DMREF: Organic Materials Architectured for Researching Vibronic Excitations with Light in the Infrared (MARVEL-IR)
  • 批准号:
    2323669
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $40.0万
  • 财政年份:
    2023
  • 负责人:
    Bryan Wong
  • 依托单位:
EAGER: CDS&E: Field Programmable Gate Arrays (FPGAs) for Enhancing the Speed and Energy Efficiency of Quantum Chemistry Simulations
  • 批准号:
    2028365
  • 项目类别:
    Standard Grant
  • 资助金额:
    $23.07万
  • 财政年份:
    2020
  • 负责人:
    Bryan Wong
  • 依托单位:
D3SC: Data-Driven Modeling and Experimental Investigation for Discovery of Aquatic Chemistry Reaction Kinetics: New Tools for Water Reuse Applications
  • 批准号:
    1808242
  • 项目类别:
    Standard Grant
  • 资助金额:
    $43.93万
  • 财政年份:
    2019
  • 负责人:
    Bryan Wong
  • 依托单位: