Aneesur Rahman Prize for Computational Physics (2020): What have we learned from Dynamical Mean Field Theory and what lies ahead?
Invited
Abstract
Dynamical Mean-Field Theory (DMFT) provides an original physical perspective on strongly correlated electron materials, as well as an efficient computational framework to understand and predict their properties. In this talk, I will review the main ideas at the heart of the DMFT construction and physical perspective. Through select examples, I will outline how the efforts of a whole community over almost three decades have managed to develop the theory to such a point that it can successfully be applied to a real material, taking into account its structure and chemical composition. I will also outline how the theory is being extended and generalized in many fruitful directions.
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Presenters
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Antoine Georges
Collège de France, Paris and Flatiron Institute, New York, Simons Foundation, Center for Computational Quantum Physics, Flatiron Institute, Center of Computational Quantum Physics, Flatiron Institute, New York City, USA, College de France
Authors
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Antoine Georges
Collège de France, Paris and Flatiron Institute, New York, Simons Foundation, Center for Computational Quantum Physics, Flatiron Institute, Center of Computational Quantum Physics, Flatiron Institute, New York City, USA, College de France