Uploaded March 2026 | Updated September 2026, 23 hours ago
Large language models (LLMs) are opening new frontiers in scientific research, enabling capabilities ranging from literature retrieval and hypothesis generation to experiment planning and operation. In this talk, I will first present quantitative evidence that LLMs encode substantial scientific knowledge and that appropriate sampling/search strategies can reliably extract it. I will then focus on a key missing ingredient for applying LLMs to discovery in the natural sciences: automating objective-function design. To address this, I will introduce the Scientific Autonomous Goal-Evolving Agent (SAGA), which analyzes optimization outcomes, proposes improved objectives, and translates them into computable scoring functions with end-to-end validation. I will demonstrate SAGA across diverse discovery settings, including antibiotic design, inorganic materials design, functional DNA sequence design, and chemical process design. Finally, I will summarize lessons learned and discuss implications for the next generation of generalist scientific agents.
Yuanqi Du is a PhD candidate in Computer Science at Cornell University. His research focuses on developing principled and efficient probabilistic and geometric modeling methods that are inspired by, and accelerate, discovery in the natural sciences, spanning chemistry, physics, and biology. His work has appeared at leading machine learning venues (NeurIPS, ICML, ICLR) and in scientific journals including Nature, Nature Machine Intelligence, Nature Computational Science, and the Journal of the American Chemical Society, including three cover articles. As a passionate community builder, he has organized over 20 events, including conferences, workshops, and seminar series on topics ranging from AI for Science, probabilistic machine learning, and learning on graphs.
Large language models (LLMs) are opening new frontiers in scientific research, enabling capabilities ranging from literature retrieval and hypothesis generation to experiment planning and operation. In this talk, I will first present quantitative evidence that LLMs encode substantial scientific knowledge and that appropriate sampling/search strategies can reliably extract it. I will then focus on a key missing ingredient for applying LLMs to discovery in the natural sciences: automating objective-function design. To address this, I will introduce the Scientific Autonomous Goal-Evolving Agent (SAGA), which analyzes optimization outcomes, proposes improved objectives, and translates them into computable scoring functions with end-to-end validation. I will demonstrate SAGA across diverse discovery settings, including antibiotic design, inorganic materials design, functional DNA sequence design, and chemical process design. Finally, I will summarize lessons learned and discuss implications for the next generation of generalist scientific agents.
Yuanqi Du is a PhD candidate in Computer Science at Cornell University. His research focuses on developing principled and efficient probabilistic and geometric modeling methods that are inspired by, and accelerate, discovery in the natural sciences, spanning chemistry, physics, and biology. His work has appeared at leading machine learning venues (NeurIPS, ICML, ICLR) and in scientific journals including Nature, Nature Machine Intelligence, Nature Computational Science, and the Journal of the American Chemical Society, including three cover articles. As a passionate community builder, he has organized over 20 events, including conferences, workshops, and seminar series on topics ranging from AI for Science, probabilistic machine learning, and learning on graphs.
