Density Functional Theory (DFT) provides a practical route for calculating the electronic structure of matter at all levels of aggregation. Five decades after its inception, it is now routinely used in many fields of research, from materials engineering to drug design. Time-dependent Density Functional Theory (TDDFT) has extended the success of DFT to time-dependent phenomena and excitations. Most applications are carried out in the linear-response regime to describe excitation and emission spectra, but the theory is applicable to much broader classes of problems, including ultrafast and nonlinear phenomena as well as non-adiabatic dynamics of coupled electron-nuclear systems. The tutorial will provide an introduction to the basic formalism of DFT and TDDFT, an overview of state-of-the-art functionals, applications, and open questions, as well as a discussion of recent efforts to combine DFT with machine learning.
Topics
- DFT: Basic theorems of ground-state DFT, with simple examples; exchange-correlation functionals and exact conditions such as scaling, self-interaction, and derivative discontinuities; exact exchange and beyond; the Jacob’s ladder of Density Functional approximations. Extensions to warm dense matter and ensembles may also be covered.
- TDDFT: Basic theorems of TDDFT, with simple examples; survey of time-dependent phenomena; memory dependence; linear response and excitation energies; optical processes and collective modes in materials; multiple and charge-transfer excitations; strong-field and ultrafast processes; non-adiabatic electron-nuclear dynamics.
- DFT and Machine learning (ML): Overview of ML models; various ways of constructing new functionals using ML; illustrations with simple model systems; using ML to accelerate large-scale DFT calculations.
Who should attend?
Graduate students, post-docs, and other scientists interested in learning about the essential elements of modern Density Functional Theory, both in its ground-state and time-dependent formulations. The tutorial talks will be pedagogical, covering the fundamentals of the theory and a few applications, latest developments including machine learning, and open questions. This tutorial will be a good introduction for the countless talks using DFT at this APS meeting.
Organizer
- Carsten Ullrich, University of Missouri-Columbia
Presenters
- John Perdew, Tulane University
- Adam Wasserman, Purdue University
- Neepa Maitra, Rutgers University, Newark
- Kieron Burke, University of California, Irvine