"Machine learning in chemistry and beyond" (ChE-651) seminar by Yuanqi Du: "Assessing Chemistry Knowledge in Large Language Models"
Event details
| Date | 27.05.2025 |
| Hour | 15:15 › 16:15 |
| Speaker | Yuanqi Du is a graduating PhD student at the Department of Computer Science, Cornell University, studying AI and its intersection with scientific discovery. His research interests include geometric models and probabilistic models (language models, generative models, sampling, stochastic control, optimal transport), and their applications in molecular simulation and discovery. Aside from his research, he is passionate about education and community building. He leads the organization of a series of events such as the Learning on Graphs conference and AI for Science, Probabilistic Machine Learning workshops at ML conferences and an educational initiative (AI for Science101) to bridge the AI and Science community. |
| Category | Conferences - Seminars |
The emerging capabilities of large language models (LLMs) are opening new frontiers in chemistry research, including experiment operation, literature retrieval, and molecular design. A central question, however, is whether LLMs truly encode chemistry knowledge—and if so, how this knowledge can be systematically extracted. In this talk, I will present an affirmative answer to this question, supported by strong empirical evidence. I will begin by framing knowledge extraction as a search problem with a computational verifier. I will illustrate through three problems: molecular optimization, crystal structure generation, and retrosynthesis. In all three cases, LLMs demonstrate impressive performance compared to state-of-the-art computational approaches. I will conclude by reflecting on analogous discoveries in other scientific domains and highlighting key questions for future exploration.
Practical information
- General public
- Free
Organizer
- Andres M Bran, Rebecca Neeser, Philippe Schwaller
Contact
- Andres M Bran, Rebecca Neeser, Philippe Schwaller