Session #5 will bring together students whose work in (TD)DFT is based on Machine Learning. Here is an overview of the presentations we will have the pleasure to listen to:
- Lenz Fiedler (Attila Cangi’s group): DFT surrogate modeling with the materials learning algorithms (MALA) – Theoretical Background
- Johannes Gedeon (Miguel Marques’s group): Machine learning the derivative discontinuity of density-functional theory
- Kanun Pokharel (Jianwei Sun’s group): Constrained Machine Learning de-orbitalization of meta-GGA exchange-correlation functionals
- Bhupalee Kalita (Kieron Burke’s group): Machine learning density functionals: Testing the Kohn-Sham regularizer for strongly and weakly correlated systems
- Karnamohit Ranka (Christine Isborn’s group): Statistical learning of electron dynamics in molecular systems
We hope to see many of you on August 3rd for this very promising session, which will be chaired by Thomas E. Baker (University of York).
The (TD)DFT Student Seminar Series is brought to you by the Department of Physics, at Rutgers Newark, The State University of New Jersey.