AI4Science Colloquium
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Previous Colloquium
Density functional theory and machine learning: The race to find the next best approximation
Date: Tuesday 11 June 2024 at 11:00-1200 CET
Place: Science Park 904, Room A1.28
Speaker: Kieron Burke, University of California at Irive, USA.
Abstract:
At least 50,000 papers each year report the results of Kohn-Sham density functional calculations for materials and molecular properties. This is a huge worldwide effort, growing rapidly with computer power and powerful machine-learning algorithms to search for desired properties. But all these calculations are limited by the accuracy and generality of our current approximations. I will discuss the race to use machine learning to find the next best approximate functional, and the potential impact.
Bio:
Professor Kieron Burke in a distinguished professor in the Chemistry department and the Physics department at UC Irvine. Prof. Burke is well-known for his fundamental work on Density Functional Theory. The “B” in the popular “PBE”-functional stands for Burke, so several of us have probably used this DFT functional that he co-developed. He has worked extensively on the DFT formalism, extensions to new areas, and applications in chemistry, materials science, matter under extreme conditions, and so forth. Recently, he has also focused on errors in DFT and machine learned density functionals.
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