NRT ‘Hackathon’ course CHEG/CISC/MSEG/ELEG 848 Computing & Data Science for Soft Materials Innovation

  • Spring 2023 

In Spring 2023, Prof. Arthi Jayaraman (Department of Chemical and Biomolecular Engineering & Department of Materials Science and Engineering) and Prof. Austin Brockmeier (Department of Electrical and Computer Engineering and Data Science Institute) co-taught this first official NRT program “hackathon” course. Click for Syllabus in Spring 2023.

The enrolled students were NRT trainees as well as other graduate students from  UD – Chemical and Biomolecular engineering, UD- Chemistry, UD- Biomedical Engineering, UD- Materials Science and Engineering and DSU- Chemistry.

Instructors introduced this interdisciplinary class of students to the technical topics of molecular modeling and simulation and machine learning with emphasis towards application of these tools to soft materials problems. Students were also engaged in effective oral and written communications training via active learning exercises in class, literature review presentations, and project oral and written presentations. These activities were geared towards students’ professional preparation for  collaborative interdisciplinary work environments.

Simultaneously, through out the semester the enrolled students were split into four interdisciplinary teams with each team tackling one project from the projects submitted by chemical industry. Here are the final reports describing how the teams of students tackled (or “hacked on”) these four industry problems.

(These reports were prepared by the students and have been approved by these industry mentors)

  • Spring 2022 

In Spring 2022, Prof. Arthi Jayaraman (Departments of Chemical and Biomolecular Engineering & Materials Science and Engineering)  and Prof. Sunita Chandrasekaran (Department of Computer and Information Sciences) co-taught the pilot version of the NRT “hackathon” course. Click here for Course Syllabus in Spring 2022

The enrolled graduate students came from different departments within UD – Chemical and Biomolecular engineering,  Chemistry, Computer and Information Sciences, Electrical and Computer Engineering, and Materials Science and Engineering.

Prof. Jayaraman and Prof. Chandrasekaran introduced this interdisciplinary class of students to the technical topics of molecular modeling and simulation, high performance computing, and machine learning (with additional guest lectures from Prof. Austin Brockmeier from the Department of Electrical and Computer Engineering) with emphasis towards application of these tools to soft materials problems. Students were also engaged in effective oral and written communications training via active learning exercises in class, literature review presentations, and project oral and written presentations. These activities were geared towards students’ professional preparation for interdisciplinary work environments.

Simultaneously, through out the semester the enrolled students were split into four interdisciplinary teams with each team tackling one project from the projects submitted by industry and national labs. Here are the problem statements and the final reports describing how they tackled these problems.