Conditional Probability Density Functional Theory - IPAM at UCLA

Explore a groundbreaking approach to electronic structure calculations in this 51-minute lecture by Kieron Burke from the University of California, Irvine. Delve into Conditional Probability Density Functional Theory (CPDFT), a novel method developed during the pandemic that addresses long-standing issues in traditional Density Functional Theory (DFT). Learn how CPDFT resolves challenges in ground-state strong correlation and high-temperature warm dense matter simulations. Examine the theory's foundations, including conditional probabilities, Hohenberg-Kohn mapping, and approximate CP-KS potentials. Investigate applications to various systems such as the uniform electron gas, Hooke's atom, and the hydrogen dimer dissociation. Compare CPDFT results with traditional methods and explore its potential for more complex systems. Gain insights into the origins, limitations, and future prospects of this innovative approach to quantum mechanics calculations.