CDS&E: Elucidating the Structure and Catalytic Activity of Nanoparticles Under Catalytic Conditions Using Ab Initio Machine Learning Force Fields

CDS

基本信息

  • 批准号:
    2245120
  • 负责人:
  • 金额:
    $ 29.13万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-08-15 至 2026-07-31
  • 项目状态:
    未结题

项目摘要

The manufacture of commercial and consumer chemical products relies heavily on catalytic reaction processes that consume a significant fraction of our nation's energy resources and are a major contributor to the emission of green-house gases. These catalysts often are composed of a metallic catalyst nanoparticle attached to a support of different material and can take on a nearly infinite number of configurations, shapes, compositions, and process operating conditions. To probe this vast parameter space for the optimal catalyst system purely by experimentation would be impossible and so efficient simulation tools are needed to explore catalyst behavior at the atomistic level. This proposal seeks to develop these simulation tools with emphasis on understanding how catalyst nanoparticle shapes change during realistic reactor operating conditions; this will be made possible by the proposal’s plan to improve the computational efficiency of molecular dynamics simulations using advanced machine learning methods. The computational models will be rigorously compared against known experimental benchmarks to guide simulator development and improve its prediction accuracy. The catalysts discovered using the simulation tools developed in this research program will contribute to the decarbonization of the chemical processes and will play an important role in developing circular chemical economies. The proposed research also will create opportunities to educate the next generation of researchers and industry leaders. Undergraduate students will learn programming skills that will increase their competitiveness in emerging job fields, and their immersive research experiences will prepare them for positions at top graduate schools and careers in higher education. Macroscopic renderings of catalysts designed with this software will be generated by 3D printing to demonstrate to the public the role computations play in accelerating catalyst design.This proposal seeks to develop and apply ab initio machine learning force fields (AIMLFF) to simulate nanoparticle (NP) catalysts under realistic reaction conditions and to help elucidate the nature of catalytic active sites. This proposed research will specifically address these challenges by hypothesizing that when AIMLFFs are trained on common structural features of periodic density functional theory (DFT) calculations that the community has identified as meaningful representations of NP catalysts, AIMLFF will be able to model NP catalysts directly under reaction-relevant conditions. This work will address critical questions related to the accuracy of the AIMLFFs by making comparisons to benchmark-quality microscopic and calorimetric measurements available in the literature, and will develop a general understanding of how the shape of metal NPs and available binding sites depend on the species of metal and the support under temperature and pressure conditions representative of reaction conditions. The proposed research will also develop an improved understanding of the relationship between catalytic activity and the evolution of NPs in comparison with high-quality X-ray measurements. Fundamental knowledge will be gained on how the equilibrium shape and defect density of supported metal NPs change over realistic reaction times for systems that are too large for current simulators.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.
商业和消费化学产品的制造严重依赖催化反应过程,这消耗了我国能源的很大一部分,是温室气体排放的主要来源。这些催化剂通常由附着于不同材料的载体的金属催化剂纳米颗粒组成,并且可以呈现几乎无限数量的配置、形状、组成和工艺操作条件。纯粹通过实验来探索最佳催化剂系统的巨大参数空间是不可能的,因此需要有效的模拟工具来探索原子水平上的催化剂行为。该提案旨在开发这些模拟工具,重点是了解催化剂纳米颗粒形状在现实反应器操作条件下如何变化;该提案计划使用先进的机器学习方法提高分子动力学模拟的计算效率。计算模型将与已知的实验基准进行严格的比较,以指导模拟器的开发并提高其预测精度。使用该研究计划中开发的模拟工具发现的催化剂将有助于化学过程的脱碳,并将在发展循环化学经济中发挥重要作用。拟议的研究还将创造机会,教育下一代研究人员和行业领导者。本科生将学习编程技能,这将提高他们在新兴工作领域的竞争力,他们的沉浸式研究经验将为他们在顶级研究生院和高等教育职业生涯中的职位做好准备。使用该软件设计的催化剂的宏观渲染图将通过3D打印生成,以向公众展示计算在加速催化剂设计中所发挥的作用。该提案旨在开发和应用从头算机器学习力场(AIMLFF)来模拟纳米颗粒(NP)催化剂在现实的反应条件下,并帮助阐明催化活性位点的性质。这项拟议的研究将专门解决这些挑战,假设当AIMLFF在周期性密度泛函理论(DFT)计算的常见结构特征上接受培训时,该社区已确定为NP催化剂的有意义的表示,AIMLFF将能够直接在反应相关条件下模拟NP催化剂。这项工作将通过与文献中提供的基准质量显微镜和量热测量进行比较来解决与AIMLFF准确性相关的关键问题,并将对金属NP的形状和可用的结合位点如何取决于金属的种类以及在代表反应条件的温度和压力条件下的支持物进行一般性理解。与高质量的X射线测量相比,拟议的研究还将提高对催化活性与纳米粒子演变之间关系的理解。对于当前模拟器来说太大的系统,将获得有关支撑金属纳米颗粒的平衡形状和缺陷密度如何在实际反应时间内变化的基本知识。该奖项反映了NSF的法定使命,并且通过使用基金会的评估被认为值得支持智力优点和更广泛的影响审查标准。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Tibor Szilvasi其他文献

Tibor Szilvasi的其他文献

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{{ truncateString('Tibor Szilvasi', 18)}}的其他基金

CAREER: Microkinetic Modeling-Driven Discovery of Molecular Catalysts
职业:微动力学模型驱动的分子催化剂发现
  • 批准号:
    2339481
  • 财政年份:
    2024
  • 资助金额:
    $ 29.13万
  • 项目类别:
    Continuing Grant

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