Chemical engineering graduates assisted by artificial intelligence (AI) will have the power to speed up scientific discovery, improve the replication of research, and unlock new paths of inquiry. Before they can responsibly combine high-throughput and remote experimentation with machine-learning-driven experimental design, they need skills in three areas.
First, students need domain knowledge. “You have to know about the problem you are solving, or you can’t evaluate the solutions you find,” says John Kitchin. Second, they need a foundation in machine learning to understand the math and ideas behind the models, as well as their capabilities and limitations. Third, students need programming skills. “Even when code is written by a large language model,” says Kitchin, a professor of chemical engineering, “you need the skills to steer the LLM and to evaluate if the code is correct and doing what is needed.”
For students who want the training to integrate AI and chemical engineering more deeply, the Department of Chemical Engineering offers the Master of Science in Artificial Intelligence Engineering-Chemical Engineering (MS in AIE-ChE). These students gain practical experience applying their skills through research. In a current collaborative project, MS in AIE-ChE students are developing a benchmarking testbed for a widely-used chemical process engineering time-series dataset. They are testing machine learning and deep learning models for anomaly detection in industry.
