Resources

We collected learning resources for students who start working on AI and machine learning in chemical engineering, for example for a thesis in our group.

Generative AI and large language models

Introductions

• Generative AI for Everyone (DeepLearning.AI): a non-technical introduction to what generative AI can and cannot do.
• Neural Networks: Zero to Hero (Andrej Karpathy): a video course that builds neural networks and a GPT-style language model from scratch in Python.
• Prompt Engineering Guide: an overview of prompting techniques for large language models (LLMs).

Hands-on courses

• Hugging Face LLM Course: using, fine-tuning, and sharing transformer models with the Hugging Face libraries.
• Hugging Face AI Agents Course: building LLM-based agents that use tools.

University courses

• Stanford CS324: Large Language Models: lecture notes on the capabilities, risks, and technical foundations of LLMs.
• Stanford CS336: Language Modeling from Scratch: an advanced course on building language models from the ground up.

Generative AI in chemical engineering

• Schweidtmann, A. M. (2024). Generative artificial intelligence in chemical engineering. Nature Chemical Engineering, 1, 193.
• Rupprecht, S., Gao, Q., Karia, T., & Schweidtmann, A. M. (2026). Multi-agent systems for chemical engineering: A review and perspective. Current Opinion in Chemical Engineering, 51, 101209.
• Schulze Balhorn, L., et al. (2024). Empirical assessment of ChatGPT's answering capabilities in natural science and engineering. Scientific Reports, 14, 4998.
• Our research projects on generative AI in chemical process engineering.