Machine Learning to Improve the Exchange and Correlation Functional in DFT

Machine Learning to Improve the Exchange and Correlation Functional in DFT

Explore machine learning approaches to improve exchange and correlation functionals in density functional theory through this conference talk. Delve into the potential of neural networks as universal approximators for creating more accurate functionals. Examine two novel approaches: injecting prior physical knowledge into training procedures and incorporating physical information directly into optimization algorithms. Discover how these methods lead to data-efficient and reliable models that outperform hand-designed functionals. Consider the cautions and challenges associated with machine-learned models in quantum mechanics. Gain insights into applications for supercritical liquids, water simulations, and energy calculations.