Active learning approach to quantum embedding simulations of strongly correlated matter
Dr. Nicola Lanata
Assistant Professor, School of Physics and Astronomy, RIT
The simulation of strongly correlated matter remains a major challenge in condensed matter physics, due to the high computational cost of existing methods. In this colloquium, we will present a new approach that combines quantum-embedding (QE) frameworks with active learning techniques to overcome this obstacle. The active learning component allows us to bypass the most computationally expensive parts of QE algorithms, making them more efficient and cost-effective. We will present preliminary data of the Hubbard model, illustrating the accuracy and potential of the method. The proposed framework has the potential to be applied to systems with arbitrary stoichiometries and crystal structures, opening up new possibilities for condensed matter physics, chemistry, and materials science.
Nicola Lanatà obtained his PhD in “Theory and Numerical Simulation in Condensed Matter Systems” at the International School for Advanced Studies (SISSA-ISAS) (Italy) in October 2009. In 2015-2018, after a few years of postdoctoral experience, he joined the National High Magnetic Field Laboratory (MagLab) as a “Dirac Fellow”. He has been Assistant Professor at Aarhus University (Denmark) and Nordic Assistant Professor at NORDITA (Sweden) in 2018-2022. Since August 2022, he is tenure-track assistant professor at the RIT Department of Physics and Astronomy, and Visiting Scholar at the
Center for Computational Quantum Physics (CCQ), NY. His research activity mainly concerns the study of strongly correlated quantum matter and the development of new theoretical and computational methods.
All are welcome!
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When and Where
Open to the Public