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.
