About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
|
Symposium
|
Computational Discovery and Design of Emerging Materials
|
Presentation Title |
Computational Discovery of Strongly Correlated Quantum Matter through Downfolding |
Author(s) |
Hitesh J. Changlani |
On-Site Speaker (Planned) |
Hitesh J. Changlani |
Abstract Scope |
Due to advances in computer hardware and new algorithms, it is now possible to perform highly accurate many-body simulations of realistic materials with all their intrinsic complications. The success of these simulations leaves us with a conundrum: how do we extract useful physical models from these simulations? There is a clear need for a multi-scale approach that can "downfold" strongly correlated materials to model Hamiltonians such as the Hubbard, Heisenberg and Kitaev models, and then accurately solve this model. Knowledge of the effective Hamiltonian should help accelerate the discovery of new phases of matter, such as quantum spin liquids, in addition to contributing to predictive theories of strongly correlated matter. My talk will present progress on a formal theory of "density matrix downfolding" and also showcase work (done recently in collaboration with neutron scattering experimentalists) showing the possible existence of a S=1 spin liquid in a nickel based pyrochlore magnet. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |