CAREER: Revealing spin-state-dependent reactivity in open-shell single atom catalysts with systematically-improvable computational tools

职业：利用可系统改进的计算工具揭示开壳单原子催化剂中自旋态依赖的反应性

基本信息

批准号：
1846426
负责人：
Heather Kulik
金额：
$ 59.37万
依托单位：
Massachusetts Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1846426&HistoricalAwards=false
关键词：
CAREER Revealing spin state dependent

项目摘要

The project focuses on selective chemical conversion of hydrocarbons found in natural gas to products of value as intermediates in the manufacture of a wide range of chemicals and fuels. To that end, the project will investigate a new class of catalytic materials known as single atom catalysts (SACs), specifically by developing computational modeling tools that will aid the discovery and design of SACs. The resulting fundamental understanding will enable rational design of robust and stable SACs for targeted challenging chemical transformations, thus providing the chemical and petroleum industries with new catalysts needed to maintain our Nation's competitiveness in the chemicals and energy sectors of the economy. These research advances will form the basis of quest-based workshop activities that teach catalysis and computation to Boston-area grade 6-12 students, advancing excitement about STEM. Single atom catalysts (SACs) are emergent catalytic materials that promise to unite the scalability of heterogeneous catalysts with the activity, selectivity, and atom-economy of homogeneous catalysts, but the reactivity of SACs is poorly understood. Short-lived, sub-nanoscale SAC active sites challenge the resolution of experimental spectroscopic techniques, making computational modeling essential to building understanding of the mechanism of SAC catalysts. The project will advance understanding of how SAC structure imparts unique reactivity for critical transformations (i.e., selective partial hydrocarbon oxidation) through systematically improvable computational modeling. Although SACs are poised as a new paradigm in selective but scalable catalysts, the very features that make SACs reactive for essential catalytic transformations also make conventional computational catalysis tools (i.e., semi-local density functional theory or DFT) ill suited to predictive SAC study. This project will identify and implement needed systematic advances beyond semi-local DFT for predictive modeling of how ligand-field-influenced spin- and oxidation-state of quantum-confined metals at SAC active sites alters reactivity. Advancement of fundamental understanding of single atom catalysts will be achieved through three aims: 1) quantifying spin state-dependent reactivity of SACs for selective transformations, 2) understanding how support identity and active site configuration/disorder influences electronic structure and reactivity of SACs, and 3) developing descriptors to predict and optimize SAC activity and stability. This will enable the tailoring of SACs for selectivity, activity, and scalability needed to address the "holy grail" challenge in catalysis of partial alkane oxidation. It will overhaul simulation methods for studying unique SAC electronic structure properties, both providing accurate predictions and incorporating disorder effects in rational SAC design. Development of SACs robust for the industrial scale with earth abundant, atom economical metal use will have a profound impact on the environment. The research advances will be integrated into outreach activities in a twice-yearly workshop that teaches catalysis and computation to grade 6-12 students, advancing excitement about STEM. The workshop will introduce catalysis and bonding concepts through 3D models, and students will design catalysts in a quest game adapted from software developed as part of this project. The program will be assessed and improved by quizzes before/after the workshop. Teaching materials for classroom instruction and web tutorials posted on the PI's website and MIT OpenCourseWare will amplify the reach of the education program. This program will benefit society by advancing excitement about STEM through immersive and research-derived tools.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.

该项目的重点是选择性地将天然气中的碳氢化合物化学转化为有价值的产品，作为制造各种化学品和燃料的中间体。为此，该项目将研究一种称为单原子催化剂（SAC）的新型催化材料，特别是通过开发有助于发现和设计 SAC 的计算建模工具。由此产生的基本认识将使我们能够合理设计稳健且稳定的 SAC，以实现有针对性的、具有挑战性的化学转化，从而为化学和石油工业提供维持我们国家在化学和能源经济领域的竞争力所需的新催化剂。这些研究进展将构成基于任务的研讨会活动的基础，这些活动向波士顿地区 6-12 年级的学生教授催化和计算，提高人们对 STEM 的兴趣。单原子催化剂（SAC）是新兴的催化材料，有望将多相催化剂的可扩展性与均相催化剂的活性、选择性和原子经济性结合起来，但对 SAC 的反应性知之甚少。短寿命的亚纳米级 SAC 活性位点对实验光谱技术的分辨率提出了挑战，因此计算建模对于理解 SAC 催化剂的机理至关重要。该项目将通过系统地改进计算模型，加深对 SAC 结构如何为关键转化（即选择性部分碳氢化合物氧化）赋予独特反应性的理解。尽管 SAC 有望成为选择性但可扩展的催化剂的新范例，但使 SAC 对基本催化转化具有反应性的特征也使得传统的计算催化工具（即半局域密度泛函理论或 DFT）不适合预测 SAC 研究。该项目将确定并实施超越半局域 DFT 所需的系统性进展，以预测 SAC 活性位点处量子限制金属的配体场影响的自旋态和氧化态如何改变反应性的模型。对单原子催化剂的基本理解的进步将通过三个目标来实现：1）量化用于选择性转化的SAC的自旋态依赖性反应性，2）了解载体身份和活性位点配置/无序如何影响SAC的电子结构和反应性，以及3) 开发描述符来预测和优化 SAC 活性和稳定性。这将使 SAC 能够针对选择性、活性和可扩展性进行定制，以解决部分烷烃氧化催化中的“圣杯”挑战。它将彻底改变用于研究独特的 SAC 电子结构特性的模拟方法，既提供准确的预测，又将无序效应纳入合理的 SAC 设计中。开发用于工业规模的强大的 SAC，地球资源丰富，原子经济的金属使用将对环境产生深远的影响。这些研究进展将被纳入每年两次的研讨会的外展活动中，该研讨会向 6-12 年级的学生教授催化和计算，提高人们对 STEM 的兴趣。该研讨会将通过 3D 模型介绍催化和键合概念，学生将在根据本项目开发的软件改编的任务游戏中设计催化剂。该计划将在研讨会之前/之后通过测验进行评估和改进。 PI 网站和麻省理工学院开放课程软件上发布的课堂教学教材和网络教程将扩大教育计划的影响范围。该计划将通过沉浸式和研究衍生工具提高人们对 STEM 的兴趣，从而造福社会。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（25）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Mechanistic Insights into Substrate Positioning That Distinguish Non-heme Fe(II)/α-Ketoglutarate-Dependent Halogenases and Hydroxylases

DOI：
10.1021/acscatal.2c06241
发表时间：
2023-02-03
期刊：
ACS CATALYSIS
影响因子：
12.9
作者：
Kastner, David W.;Nandy, Aditya;Kulik, Heather J.
通讯作者：
Kulik, Heather J.

The Effect of Hartree-Fock Exchange on Scaling Relations and Reaction Energetics for C–H Activation Catalysts

DOI：
10.1007/s11244-021-01482-5
发表时间：
2021-06
期刊：
Topics in Catalysis
影响因子：
3.6
作者：
Vyshnavi Vennelakanti;Aditya Nandy;H. Kulik
通讯作者：
Vyshnavi Vennelakanti;Aditya Nandy;H. Kulik

Representations and strategies for transferable machine learning improve model performance in chemical discovery

可迁移机器学习的表示和策略提高了化学发现中的模型性能

DOI：
10.1063/5.0082964
发表时间：
2022
期刊：
The Journal of Chemical Physics
影响因子：
0
作者：
Harper, Daniel R.;Nandy, Aditya;Arunachalam, Naveen;Duan, Chenru;Janet, Jon Paul;Kulik, Heather J.
通讯作者：
Kulik, Heather J.

Harder, better, faster, stronger: Large-scale QM and QM/MM for predictive modeling in enzymes and proteins