I will discuss the details and challenges of using machine-learning to create new density functional approximations.   I plan to focus on the distinction between materials and molecules, the role of exact conditions, and the importance of using the density.  Relevant references are:

 The difference between molecules and materials: Reassessing the role of exact conditions in density functional theory Ryan Pederson and Kieron Burke, The Journal of Chemical Physics 159, 214113 (2023).
 Extending density functional theory with near chemical accuracy beyond pure water Suhwan Song, Stefan Vuckovic, Youngsam Kim, Hayoung Yu, Eunji Sim, and Kieron Burke, Nature Communications 14, 799 (2023).
 Machine learning and density functional theory Ryan Pederson, Bhupalee Kalita, and Kieron Burke, Nature Reviews Physics (2022).
 Improving Results by Improving Densities: Density-Corrected Density Functional Theory Eunji Sim, Suhwan Song, Stefan Vuckovic, and Kieron Burke, Journal of the American Chemical Society 144, 6625-6639 (2022).
 Calculation and interpretation of classical turning surfaces in solids Aaron D. Kaplan, Stewart J. Clark, Kieron Burke, and John P. Perdew, npj Computational Materials 7, 2057-3960 (2021)