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.


Kelvin is currently a Senior Scientific Machine Learning engineer at NVIDIA, where he works on developing high-performance AI workflows to the chemical and physical sciences. Kelvin completed his PhD on laser-induced reaction dynamics in 2017 at the University of New South Wales in Sydney, Australia, under the guidance of Drs. Scott Kable and Meredith Jordan. Afterwards, he held postdoctoral research positions at the Center for Astrophysics | Harvard & Smithsonian (2017 - 2020), working with Dr. Michael McCarthy, and MIT Chemistry (2020 - 2021), working with Dr. Brett McGuire on problems ranging from high-resolution molecular spectroscopy, to automated analysis of astronomical spectra to machine learning for unknown molecule identification. Kelvin is the proud father of two cats, lives in the beautiful Pacific Northwest, and lists hiking and sim racing as his hobbies.