Upcoming Event: CosmicAI Seminar

Accelerating (Astro)chemical discovery with machine learned atomistic models and Computer Vision for Scientific Discovery AND Computer Vision for Scientific Discovery

Kelvin Lee & Zezhou Cheng, Senior Scientific Machine Learning Engineer (NVIDIA) & Assistant Professor (University of Virginia)

Abstract

Part 1 Abstract (Kelvin Lee): Among the many rapidly developing and expanding fields in AI/ML, applications to atomistic modeling are perhaps one of the most exciting, owing to advances in model architectures and the growing availability of large-scale datasets. Perhaps most exemplary of these capabilities are those used in biomolecular modeling, such as those from the AlphaFold/RoseTTAFold families, which were awarded the 2024 Nobel Prize in Chemistry. These models feature a host of capabilities, including the ability to respect Euclidean symmetries.

While it may not have received nearly as much public attention, the same modeling principles are invariant to translation when applied outside the life sciences, namely in the chemical and materials sciences. In this talk, I will discuss some of the recent advances in the design of machine learned interatomic potentials, how they're trained, and their capabilities and applications in fully atomistic simulations. In the final part of my talk, I will also discuss some potential use cases where the same models can potentially be used to drive astrochemical and observational/spectroscopic discovery.

Part 2 Abstract (Zezhou Cheng): Artificial intelligence has recently made remarkable contributions across scientific fields. Within AI, computer vision—focused on enabling machines to see and interpret the 3D visual world—has become a key driver of progress. In this talk, I will first highlight our efforts in applying computer vision to pressing challenges in climate change and materials discovery. I will then present our advances in 3D reconstruction and scene understanding, a core task in computer vision. Finally, I will discuss the broader potential of these techniques to accelerate discovery in astronomy and beyond.