Enabling high-throughput computational discovery of stable and active single-site oxidation catalysts

实现稳定和活性单中心氧化催化剂的高通量计算发现

• 批准号: 1704266
• 负责人: Heather Kulik
• 金额: $ 31.72万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1704266&HistoricalAwards=false
• 关键词: Enabling; throughput; computational; discovery; stable

The project will advance computational tools for the discovery, design, and mechanistic understanding of factors promoting reactions on catalysts containing a single, highly-specific active site. Such single-site catalysts offer unique opportunities for improving the activity and product selectivity of a range of catalytic reactions. The computational tool development will focus on the dehydrogenation of light alkanes that comprise the major component of natural gas. The abundance of shale gas resources has brought increasing need for more efficient catalysts for transforming the relatively unreactive alkanes to more reactive species of value as intermediates in the manufacture of a wide range of chemicals and fuels. Development of the new computational tools will hasten the identification of efficient single-site catalysts, 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. Single-site catalysts present unique opportunities for high-selectivity and activity, but challenges remain for their study and design. Computational catalysis has emerged as a powerful tool - when coupled with experimental studies - to guide the design of catalysts based on fundamental structure-activity relationships rather than the inefficient trial-and-error approach often used. The project will advance computational tools for discovery, design, and mechanistic study of single-site catalysts. The computational platform will combine new methodology for catalyst structure building and development, with methods for improving first-principles prediction accuracy. New iterative screening approaches will impart fundamental understanding to structure-property relationships that give rise to gas phase oxidant activation for selective C-H activation with high stability. The research efforts will focus on three aims: 1) building high-throughput single-site catalyst structure generation tools; 2) developing robust augmented-DFT (density functional theory) predictions for energetics; and 3) generating iterative design strategies for discovering stable and active single-site catalysts. These three aims will bring to bear a robust, open-source platform for the discovery of catalysts needed to capitalize on the abundant energy and chemical feedstock reserves found in shale resources. From a broader perspective, the open-source software tools and structure-property correlations will provide benefit to the computational catalysis community beyond the single-site catalysts studied in this work. The project integrates the education of graduate and undergraduate students with innovative research in computational catalyst design. The research will be shared with the community through a hands-on workshop at the Massachusetts Institute of Technology Museum for grades 6-12 students in Boston-area schools, with a special emphasis on engaging female and underrepresented minorities in STEM. Local efforts will be augmented by online tutorials that teach chemistry, catalysis, and electronic structure to a general audience.

该项目将推动对发现，设计和机械理解的计算工具，以了解促进包含一个高度特异性活性位点的催化剂上反应的因素。 这种单位催化剂为提高一系列催化反应的活性和产品选择性提供了独特的机会。 计算工具的开发将集中于构成天然气主要组成部分的轻烷烃的脱氢。 众多的页岩气资源使对更有效的催化剂的需求越来越多，将相对不反应性的烷烃转变为更具反应性的价值物种，因为中间体在各种化学物质和燃料的制造中都有中间体。 新的计算工具的开发将加快识别有效的单位催化剂，从而为化学和石油行业提供所需的新催化剂，以维持我们国家在经济的化学和能源领域的竞争力。 单位催化剂为高选择性和活动带来了独特的机会，但其研究和设计仍然存在挑战。 当与实验研究结合使用时，计算催化已成为一种强大的工具，以指导基于基本结构 - 活性关系的催化剂设计，而不是经常使用的效率低下的反复试验方法。 该项目将推进单位催化剂的发现，设计和机械研究的计算工具。 计算平台将结合催化剂结构建设和开发的新方法，以及提高第一原理预测准确性的方法。 新的迭代筛选方法将使结构 - 特性关系具有基本理解，从而导致气相氧化剂激活以高稳定性的选择性C-H激活。 研究工作将重点放在三个目标上：1）建立高通量单位催化剂结构生成工具； 2）为能量学开发强大的增强DFT（密度功能理论）预测； 3）生成迭代设计策略，以发现稳定和活跃的单位催化剂。 这三个目标将带来一个强大的开源平台，以发现资本在页岩资源中发现的丰富能量和化学原料储量所需的催化剂。 从更广泛的角度来看，开源软件工具和结构 - 培训相关性将为计算催化社区提供好处，而不是这项工作中研究的单一站点催化剂。 该项目将研究生和本科生的教育与计算催化剂设计的创新研究相结合。 这项研究将通过马萨诸塞州理工学院博物馆的动手讲习班与社区分享，以在波士顿地区学校为6至12年级的学生提供，特别强调与STEM中的女性和代表性不足的少数群体吸引。 在线教程将对一般受众教授化学，催化和电子结构的在线教程进行增强当地的努力。

项目成果

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

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
• 作者: Harper, Daniel R.;Nandy, Aditya;Arunachalam, Naveen;Duan, Chenru;Janet, Jon Paul;Kulik, Heather J.
• 通讯作者: Kulik, Heather J.

Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry

• DOI: 10.1021/acs.iecr.8b04015
• 发表时间: 2018-10-24
• 期刊: INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
• 影响因子: 4.2
• 作者: Nandy, Aditya;Duan, Chenru;Kulik, Heather J.
• 通讯作者: Kulik, Heather J.

Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry

• DOI: 10.1021/acs.inorgchem.9b00109
• 发表时间: 2019-08-19
• 期刊: INORGANIC CHEMISTRY
• 影响因子: 4.6
• 作者: Janet, Jon Paul;Liu, Fang;Kulik, Heather J.
• 通讯作者: Kulik, Heather J.

Biochemical and crystallographic investigations into isonitrile formation by a nonheme iron-dependent oxidase/decarboxylase.

• DOI: 10.1074/jbc.ra120.015932
• 发表时间: 2021-01
• 期刊: The Journal of biological chemistry
• 作者: Jonnalagadda R;Del Rio Flores A;Cai W;Mehmood R;Narayanamoorthy M;Ren C;Zaragoza JPT;Kulik HJ;Zhang W;Drennan CL
• 通讯作者: Drennan CL