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.

该项目的重点是在天然气中发现的碳氢化合物的选择性化学转化，以在多种化学物质和燃料制造中作为中间体的价值产物。为此，该项目将研究一种新的催化材料，称为单原子催化剂（SAC），特别是通过开发有助于发现和设计SAC的计算建模工具。由此产生的基本理解将使稳健和稳定的囊的合理设计用于有针对性的化学转化，从而为化学和石油行业提供了维持我们国家在经济的化学和能源领域的竞争力所需的新催化剂。这些研究的进步将构成基于任务的研讨会活动的基础，这些研讨会活动教授向波士顿地区6-12年级学生进行催化和计算，从而促进了对STEM的兴奋。单原子催化剂（SAC）是新兴的催化材料，有望将异质催化剂的可伸缩性与活性，选择性和均质催化剂的原子经济结合在一起，但囊囊的反应性知之甚少。短暂的，亚纳米级的SAC活性位点挑战了实验光谱技术的分辨率，从而使计算建模对于建立对SAC催化剂机制的理解至关重要。该项目将通过系统地改进的计算建模来促进对SAC结构如何赋予关键转换（即选择性部分烃氧化）的独特反应性。尽管SAC在选择性但可扩展的催化剂中被固定为一种新的范式，但使SAC对基本催化转化的反应性的特征也使传统的计算催化工具（即半本地密度的功能理论或DFT）不适合预测性SAC研究。该项目将识别并实施超出半本地DFT的所需系统进步，以预测SAC活动位点上量子限制金属的配体 - 场影响和氧化状态如何改变反应性的反应性。对单个原子催化剂的基本理解的进步将通过三个目的来实现：1）量化囊囊的自旋状态依赖性反应性在选择性转化中，2）了解支持身份和主动位点配置/障碍如何影响囊的电子结构和反应性，以及3）为预测和优化SAC活性和稳定性的描述符。这将使SAC为解决部分烷烃氧化催化中“圣杯”挑战所需的选择性，活性和可伸缩性量身定制。它将大修模拟方法用于研究独特的SAC电子结构特性，既可以提供准确的预测，又可以在理性SAC设计中纳入混乱效应。用地球丰富，原子经济的金属使用对工业规模的囊肿开发将对环境产生深远的影响。该研究进展将在每年一次的一次研讨会中纳入外展活动，该研讨会教授催化和计算为6至12年级的学生，并激发了对STEM的兴奋。该研讨会将通过3D模型引入催化和粘结概念，学生将在Quest游戏中设计催化剂，该催化剂是根据该项目的一部分开发的软件改编的。研讨会之前/之后的测验将评估和改进该计划。在PI网站和MIT OpenCourse软件上发布的课堂教学和网络教程的教学将扩大教育计划的范围。该计划将通过沉浸式和研究衍生的工具来提高对STEM的兴奋，从而使社会受益。该奖项反映了NSF的法定使命，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准通过评估来支持的。

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