Recorded 26 May 2022. Kieron Burke of the University of California, Irvine, presents "Conditional probability density functional theory" at IPAM's Monte Carlo and Machine Learning Approaches in Quantum Mechanics Workshop. Abstract: During the pandemic, in collaboration with Steve White (UCI) and Robert Evans (U Bristol), we developed a new approach to electronic structure calculations. It is more expensive than traditional DFT, but also resolves some long-standing problems for DFT, including ground-state strong correlation and high-temperature warm dense matter simulations. It would be wonderful if someone could run CPDFT calculations for more complex systems than the models we tested it on. Conditional probability density functional theory Ryan Pederson, Jielun Chen, Steven R. White, Kieron Burke, (2022). arXiv:2203.09647 Correlation energy of the uniform gas determined by ground state conditional probability density functional theory Dennis Perchak, Ryan J. McCarty and Kieron Burke, Phys. Rev. B 105, 165143 (2022). Bypassing the Energy Functional in Density Functional Theory: Direct Calculation of Electronic Energies from Conditional Probability Densities Ryan J. McCarty, Dennis Perchak, Ryan Pederson, Robert Evans, Yiheng Qiu, Steven R. White, and Kieron Burke Phys. Rev. Lett. 125, 266401 (2020) Learn more online at: http://www.ipam.ucla.edu/programs/workshops/workshop-iv-monte-carlo-and-machine-learning-approaches-in-quantum-mechanics/?tab=schedule