# AI for Chemical Engineers Course

## Overview of the Course and Repo
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Chemical industries are rapidly transitioning toward intelligent manufacturing and innovation with AI as the central enabling technology. It is essential for all chemical engineering students to understand what AI is, how it works, where it can be applied, and what its limitations and potential pitfalls are. While AI/ML courses for chemical engineering are emerging nationwide, most target students interested in computational research careers.
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**This repo contains course materials for "AI for Chemical Engineers", an AI/ML literacy, elective course that was designed to be accessible and appealing to all junior and senior ChemE undergraduate and graduate students.** The course was first taught in Fall 2025 in the [Department of Chemical & Biological Engineering at the University at Buffalo](https://engineering.buffalo.edu/chemical-biological.html) and received exceptional feedback from both undergraduate and graduate attendees. 

Some highlights of this course:
- Structured to help ChemE students develop fundamental abilities to understand, critically evaluate, and interact with AI/ML systems. 
- Covers a comprehensive spectrum of AI/ML concepts: AI-assisted programming, predictive modeling (e.g., regression and classification), prescriptive algorithms (e.g., genetic algorithm), and generative AI (e.g., transformer), emphasizing breadth over depth. 
- Narrative lectures on AI/ML algorithms are accompanied by highly interactive hands-on demonstrations in the context of chemical science and engineering. 
- Seamlessly integrated latest foundational generative AI technologies, such as large language models (LLMs; e.g., Gemini), throughout the course to assist students with learning tasks. 
- Features guest lectures from leading scientists and engineers in both academia and industry (see [past guest speakers](#acknowledgment)). 
- Touches on topics like AI ethics, safety, and limitations in academic and industrial settings. 

Repo structure:
- `Hands-on-tutorials`: Interactive tutorials in Python Jupyter notebook in the context of chemical engineering.
- `Lecture-slides`: PDF copies of lecture slides 

## Course Schedule

For full details regarding course objectives, recommended textbooks, homework, and final projects, please refer to the [syllabus](/CE400-500_syllabus_Fall2025.pdf).

| # | Week | Topic |
|---|---|---|
| 1 | 1 | Introduction of course, history of AI/ML |
| 2 | | Demonstration of latest AI/ML technology, part 1: LLMs, text reasoning, media generation |
| 3 | 2 | Demonstration of latest AI/ML technology, part 2: Media generation, vibe coding, agentic AI |
| 4 | | Introduction to AI-assisted Python programming, part 1: Python basics, AI coding assistant |
| 5 | 3 | Introduction to AI-assisted Python programming, part 2: Python basics, third-party packages, example problems in ChemE |
| 6 | | Introduction to predictive AI & supervised learning workflow |
| 7 | 4 | Introduction to supervised learning workflow, hands-on practice on linear regression for wine quality prediction |
| 8 | | Hands-on practice on linear regression for wine quality prediction |
| 9 | 5 | Introduction to classification tasks (confusion matrix, classification evaluation metrics)|
| 10 | | Hands-on practice on tree-based models for process fault detection  |
| 11 | 6 | Introduction to neural networks: Basic concepts, backpropagation, popular architecture, explainable AI |
| 12 | | Hands-on practice on multilayer perceptron to predict hydrogen storage in MOFs |
| 13 | 7 | Hands-on practice (continued), mid-term journal review presentation |
| 14 | | Mid-term journal review presentation |
| | 8 | No class – Fall Break |
| 15 | | Introduction to unsupervised learning: Clustering (k-means etc.) and dimensionality reduction (UMAP, PCA etc.) |
| 16 | 9 | Introduction to prescriptive AI: Deterministic & stochastic optimization, exploration vs exploitation, genetic algorithm, Bayesian optimization |
| 17 | | Introduction to prescriptive AI (cont’d), hands-on practice of applying genetic algorithm to optimize synthesis conditions of MOFs |
| 18 | 10 | Introduction to generative AI: Latent/embedding space, variation autoencoder, diffusion model, generative adversarial network |
| 19 | | Hands-on practice of implementing variational autoencoder for drug discovery |
| | 11 | No class – AIChE Meeting |
| | | No class – AIChE Meeting |
| 20 | 12 | Introduction to LLM, hands-on practice of building an LLM-powered agent to automate the drug discovery |
| 21 | | Guest lecture on AI in materials science |
| 22 | 13 | Guest lecture on AI in chemical industry |
| 23 | | Guest lecture on AI in biological engineering |
| 24 | 14 | Guest lecture on AI in chemical industry  |
| | | No class – Thanksgiving |
| 25 | 15 | Final project presentation |
| 26 | | Final project presentation |

## Acknowledgment

This course significantly benefits from the open-source AI/ML materials shared by the community. We also thank all guest lecturers for this course from academia and industry:
*   Prof. Jiayu Peng (MDI, UB, Fall 2025)
*   Prof. Rudiyanto Gunawan (CBE, UB, Fall 2025)
*   Dr. Bea Braun (Dow Chemical, Fall 2025)
*   Dr. Alix Schmidt (Dow Chemical, Fall 2025)
*   Dr. Matt Barker (P&G, Fall 2025)

## License

Except as otherwise noted:

- All course materials (slides, text, and figures) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
- Source code in this repository is licensed under the Apache License 2.0.