Title: Unlocking unprecedented domains in computational chemistry with massive open AI models and datasets
Unlocking unprecedented domains in computational chemistry with massive open AI models and datasets
OMol25: Open Molecules 2025, or OMol25, is a collection of more than 100 million 3D molecular snapshots whose properties have been calculated with density functional theory (DFT). DFT is an incredibly powerful tool for modeling precise details of atomic interactions, allowing scientists to predict the force on each atom and the energy of the system, which in turn dictate the molecular motion and chemical reactions that determine larger-scale properties, such as how the electrolyte reacts in a battery or how a drug binds to a receptor to prevent disease.
UMA: UMA models are trained on half a billion unique 3D atomic structures (the largest training runs to date) by compiling data across multiple chemical domains, e.g. molecules, materials, and catalysts.
Event Schedule:
11:45 AM – Doors Open
12:00 PM – Presentation
1:00 PM – Reception
RSVP:
Please RSVP here.
Note: As of Aug 28th, the location of this event has changed from the Grimes Center to Banatao Auditorium at 310 Sutardja Dai Hall.
Our Speakers:
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Dr. Samuel M. Blau is a Research Scientist at Berkeley Lab working at the intersection of computational chemistry, materials science, high-performance computing, and machine learning. He received his B.S. in 2012 from Haverford College and his Ph.D. in Chemical Physics from Harvard University in 2017. Sam has pioneered the use of self-correcting molecular simulation workflows to enable the construction of chemical reaction networks describing complex reaction cascades, e.g. those responsible for battery interphase formation and photoresist patterning. Sam’s research group also develops novel datasets, representations, and models for machine learning of chemistry and materials as well as methods that leverage ML model speed and differentiability for accelerated scientific discovery.
Aditi Krishnapriyan is an Assistant Professor at UC Berkeley where she is part of Chemical and Biomolecular Engineering, Electrical Engineering and Computer Sciences, and Berkeley AI Research; as well as a faculty scientist in the Applied Mathematics division at Lawrence Berkeley National Laboratory. Her research interests include physics-inspired machine learning methods; geometric deep learning; inverse problems; and development of machine learning methods informed by physical sciences applications including molecular and fluid dynamics. A former DOE Computational Science Graduate Fellow, she holds a PhD from Stanford University and in 2020–2022 was the Luis W. Alvarez Fellow in Computing Sciences at Lawrence Berkeley National Laboratory.
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Dr. Ray M. Gao is currently a Staff Research Engineer at Meta FAIR Labs, working on AI for science and atomistic chemistry. Previously, he worked on a wide range of AI, computer vision, and robotics problems in the industry, from perception for self-driving cars to navigation for autonomous UAVs to large-scale training infrastructure for LLMs and foundational models. He received his Ph.D. from the University of Toronto in experimental physics, developing methods that reached new limits of spatiotemporal resolution of coherent femtosecond electron beams and using them to capture atomic motions of molecular crystals on the femtosecond timescale for the first time.https://forms.gle/QJk2h7EjtHAthFN28