NRT-HDR: Internet of Catalysis - Harnessing Data Science for Catalyst Design

NRT-HDR：催化互联网 - 利用数据科学进行催化剂设计

• 批准号: 1922649
• 负责人: Kevin Leonard
• 金额: $ 300万
• 依托单位: University of Kansas Center for Research Inc
• 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: 2019-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1922649&HistoricalAwards=false
• 关键词: NRT; HDR; Internet; Catalysis; Harnessing

Nearly every aspect of modern life depends on catalysts, from fuels to synthetic fibers, drugs to detergents, and paints to plastics. Catalysts make and break molecular bonds, turning raw materials into useful products. With increased global demand for the products of these catalytic reactions, new and reimagined catalysts are essential to drive innovation and more effectively utilize our natural resources. Current strategies for developing catalysts rely mostly on time-intensive, trial-and-error experiments. Recent advances in computer science and machine learning have the potential to speed up discovery in this field by automating search mechanisms for these vastly complex and data-rich systems, ultimately revealing hidden patterns and physical properties that scientists can use to design novel catalysts. However, to initiate this data revolution in catalysis, highly skilled individuals are needed who are capable of collaborating across the chemical and computer sciences. This National Science Foundation Research Traineeship (NRT) award to the University of Kansas will address this need by training graduate students to harness data to ask and answer new questions needed for the discovery of safe, effective, and energy-efficient chemical conversion processes. The traineeship is designed to provide a unique, scalable and comprehensive training opportunity for fifty (50) MS and PhD students, including twenty-five (25) funded trainees, from chemical engineering, chemistry, and computer science.This NRT program will lay the groundwork for developing novel data mining and extraction methodologies, which will in turn accelerate catalytic insights and innovations with potentially far-reaching advances in challenging chemistries such as water splitting and alkane oxidation. Decades of research in these chemistries have led to thousands of publications, yet breakthrough catalysts remain elusive. With focused training in how to harness the plethora of data, researchers will establish new insights on catalyst structure-function patterns and correlations in catalytic materials, leading to potentially transformational advances in these chemistries. The research program will create modularized data mining frameworks and reveal machine learning techniques that can decipher complex property/activity relationships in other catalytic systems as well, ultimately establishing an Internet of Catalysis. This NRT program will prepare a future workforce of interdisciplinary scholars who are highly skilled communicators and leaders capable of excelling in a wide array of careers. Initiatives to promote active inclusion and graduate school resilience will transform the learning environment and encourage the full participation of women, underrepresented minorities, persons with disabilities, and veterans. Key training components include team building with a global perspective, new courses and a certificate program, hypothesis-based career planning, inclusion and resilience training, and technical training. This initiative will impact the training of graduate students across departments and institutions, contributing to the development and broad adoption of evidence-based teaching and learning practices for graduate education. The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.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.

现代生活的几乎每个方面都依赖于催化剂，从燃料到合成纤维，从药物到洗涤剂，从油漆到塑料。 催化剂制造和破坏分子键，将原材料转化为有用的产品。随着全球对这些催化反应产物需求的增加，新的和重新设计的催化剂对于推动创新和更有效地利用我们的自然资源至关重要。目前开发催化剂的策略主要依赖于时间密集型的试错实验。计算机科学和机器学习的最新进展有可能通过自动化这些非常复杂和数据丰富的系统的搜索机制来加速这一领域的发现，最终揭示科学家可以用来设计新型催化剂的隐藏模式和物理特性。然而，为了启动催化领域的这场数据革命，需要能够在化学和计算机科学领域进行合作的高技能人才。这项授予堪萨斯大学的国家科学基金会研究培训（NRT）将通过培训研究生利用数据来询问和回答发现安全，有效和节能的化学转化过程所需的新问题来解决这一需求。该培训旨在为50名MS和博士生提供独特，可扩展和全面的培训机会，其中包括25名来自化学工程，化学和计算机科学的资助学员。该NRT计划将为开发新的数据挖掘和提取方法奠定基础，这将反过来加速催化剂的洞察力和创新，在具有挑战性的化学领域（如水分解和烷烃氧化）取得潜在的深远进展。几十年来，这些化学物质的研究已经产生了数千篇出版物，但突破性的催化剂仍然难以捉摸。 通过对如何利用大量数据的集中培训，研究人员将建立对催化剂结构-功能模式和催化材料相关性的新见解，从而在这些化学领域取得潜在的变革性进展。该研究计划将创建模块化的数据挖掘框架，并揭示机器学习技术，这些技术也可以破译其他催化系统中复杂的性质/活性关系，最终建立催化互联网。该NRT计划将为未来的跨学科学者队伍做好准备，他们是高技能的沟通者和领导者，能够在广泛的职业生涯中表现出色。促进积极包容和研究生院复原力的举措将改变学习环境，鼓励妇女、代表性不足的少数民族、残疾人和退伍军人充分参与。主要培训内容包括具有全球视角的团队建设、新课程和证书课程、基于假设的职业规划、包容性和复原力培训以及技术培训。 这一举措将影响跨部门和机构的研究生的培训，有助于发展和广泛采用基于证据的教学和学习实践的研究生教育。 NSF研究培训（NRT）计划旨在鼓励为STEM研究生教育培训开发和实施大胆的，新的潜在变革模式。该计划致力于通过创新的、基于证据的、与不断变化的劳动力和研究需求相一致的综合培训模式，在高优先级的跨学科或融合研究领域对STEM研究生进行有效培训。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

Sulfur incorporation into NiFe oxygen evolution electrocatalysts for improved high current density operation

将硫掺入 NiFe 析氧电催化剂以改善高电流密度操作

• DOI: 10.1039/d2ma00902a
• 发表时间: 2023
• 期刊: Materials Advances
• 影响因子: 5
• 作者: Wang, Jiaying;Barforoush, Joseph M.;Leonard, Kevin C.
• 通讯作者: Leonard, Kevin C.

Evidence for Reactivity of Decamethylcobaltocene with Dichloromethane

十甲基钴茂与二氯甲烷的反应性证据

• DOI: 10.1021/acs.organomet.3c00176
• 发表时间: 2023
• 期刊: Organometallics
• 影响因子: 2.8
• 作者: Mikeska, Emily R.;Blakemore, James D.
• 通讯作者: Blakemore, James D.

Nanogap-Resolved Adsorption-Coupled Electron Transfer by Scanning Electrochemical Microscopy: Implications for Electrocatalysis

• DOI: 10.1021/acs.analchem.2c04008
• 发表时间: 2022-12-13
• 期刊: ANALYTICAL CHEMISTRY
• 影响因子: 7.4
• 作者: Kurapati, Niraja;Janda, Donald C.;Amemiya, Shigeru
• 通讯作者: Amemiya, Shigeru

Evaluation of Machine Learning Models on Electrochemical CO 2 Reduction Using Human Curated Datasets

使用人工管理数据集评估电化学 CO 2 还原的机器学习模型

• DOI: 10.1021/acssuschemeng.2c02941
• 发表时间: 2022
• 期刊: ACS Sustainable Chemistry & Engineering
• 影响因子: 8.4
• 作者: Farris, Brianna R.;Niang-Trost, Tevin;Branicky, Michael S.;Leonard, Kevin C.
• 通讯作者: Leonard, Kevin C.

Highly Selective Isobutane Hydroxylation by Ozone in a Pressure-Tuned Biphasic Gas–Liquid Process

压力调节双相气液工艺中臭氧高选择性异丁烷羟基化

• DOI: 10.1021/acssuschemeng.1c01004
• 发表时间: 2021
• 期刊: ACS Sustainable Chemistry & Engineering
• 影响因子: 8.4
• 作者: Zhu, Hongda;Jackson, Timothy A.;Subramaniam, Bala
• 通讯作者: Subramaniam, Bala