CDS&E: Machine learning enabled modelling of dynamic nanoparticle catalysts

CDS

• 批准号: 2152767
• 负责人: Philippe Sautet
• 金额: $ 36.43万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2152767&HistoricalAwards=false
• 关键词: CDS& Machine; learning; enabled; modelling

Catalysis enables chemical and fuels manufacturing by promoting efficient energy and resource utilization. As widely practiced in industry, catalysis is an inherently complex process, which has led to heavy reliance on trial-and-error methods for catalyst discovery and optimization. Advances in computational and data science methods are seeing increasing application in catalyst discovery and design. The project extends those methods to a range of catalyst particle sizes that are common in both industrial and environmental applications, yet challenging to simulate because of 1) the large number of atoms which must be modeled, 2) the “massive” combinatorial space of their structural configurations, and 3) dynamical changes in structure that occur in response to changes in reaction conditions. In contrast to previous modeling approaches that treat small particles as static, perfect polyhedrons, the project develops methods that consider the effects of dynamic rearrangements and metastable structures on dehydrogenation and hydrogenolysis reactions as catalyzed by platinum and nickel nanoparticles containing 20 to 200 atoms. To that end, the project employs several novel data science methods to model the vast computational space needed to predict relationships between dynamic catalyst structures and chemical reactivity. Beyond the technical aspect, the project offers educational and outreach activities focused on underrepresented students at the undergraduate and high-school levels.The project is built on the hypothesis that a transition metal nanoparticle, for example based on Pt or Ni, presents several low energy metastable isomers, representing accessible “defect-type” structures, that might fully dominate the catalytic activity of the particle. The main difficulty for atomistic modelling of metallic nanoparticles is the massive combinatorial space of their structural configurations. Those challenges will be addressed by exploiting data science methods leading to generalized models linking the structure and reactivity of nanoparticles in dynamical situations. Data science methods will be used at two places in the project, 1) interatomic potential fitting, and 2) identification of structural motifs on nanoparticle surfaces. The first step involves generation of an accurate inter-atomic potential using Neural Networks based on the investigator’s previous experience modeling small clusters. The various configurations of the surface atoms on the nanoparticle will be explored using basin-hoping algorithms, in a grand canonical approach to handle a variable number of adsorbates. This will allow us to efficiently explore the extremely diverse structures for Pt and Ni particles of ~20-200 atoms, with realistic adsorbate coverage. The obtained large structure database will be used to extract local structure descriptors and learn the statistical distribution of local structural motifs, using pattern recognition algorithms. This distribution of local motifs is key to analyze the catalytic activity. The educational and outreach aspects involve partnering with the UCLA Center for Excellence in Engineering and Diversity (CEED) to involve both high-school and undergraduate URM students in the research. A second initiative features a one-day workshop for high-school teachers on the UCLA campus, targeting Latino schools in central Los Angeles. The program will illustrate how computational chemistry can provide key insights on how catalysts work at the atomic scale.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.

催化通过促进能源和资源的高效利用来实现化学和燃料制造，催化本质上是一个复杂的过程，这导致催化剂发现和优化的进步严重依赖试错法。数据科学方法在催化剂发现和设计中的应用越来越广泛，该项目将这些方法扩展到工业和环境观测应用中常见的一系列催化剂颗粒尺寸，但由于以下原因而难以模拟：1) 必须有大量原子。建模，2）其结构配置的“巨大”组合空间，以及3）响应反应条件变化而发生的结构动态变化与之前将小颗粒视为静态、完美多面体的建模方法相比，该项目开发了考虑以下因素的方法。动态重排和亚稳态结构对含有 20 至 200 个原子的铂和镍纳米颗粒催化的脱氢和氢解反应的影响。采用多种新颖的数据科学方法来模拟预测动态催化剂结构和化学反应性之间关系所需的巨大计算空间。 除了技术方面之外，该项目还针对本科和高中阶段的代表性不足的学生提供教育和外展活动。该项目基于这样的假设：过渡金属纳米颗粒（例如基于 Pt 或 Ni 的过渡金属纳米颗粒）呈现出几种低能亚稳态异构体，代表可接近的“缺陷型”结构，这可能完全主导颗粒的催化活性。为了金属纳米粒子的原子建模是其结构配置的巨大组合空间，这些挑战将通过利用数据科学方法来解决，从而产生将动态情况下纳米粒子的结构和反应性联系起来的广义模型。数据科学方法将在两个地方使用。该项目，1）原子间势拟合，2）纳米颗粒表面结构图案的识别第一步涉及使用神经网络根据研究者之前的数据生成准确的原子间势。纳米颗粒表面原子的各种配置将使用盆地跳跃算法进行探索，以处理可变数量的吸附物的大规范方法，这将使我们能够有效地探索 Pt 的极其多样化的结构。约 20-200 个原子的 Ni 颗粒，具有真实的吸附物覆盖范围，所获得的大型结构数据库将用于提取局部结构描述符并使用模式识别算法学习局部结构图案的统计分布。分析催化活动的关键是与加州大学洛杉矶分校工程与多样性卓越中心 (CEED) 合作，让 URM 的高中生和本科生参与研究。该项目面向加州大学洛杉矶分校校园的高中教师，目标是洛杉矶市中心的拉丁裔学校。该项目将说明计算化学如何提供有关催化剂如何在原子尺度上工作的重要见解。该奖项反映了美国国家科学基金会的法定使命，并被认为是值得的。通过评估提供支持利用基金会的智力优势和更广泛的影响审查标准。