Machine Learning to Improve the Exchange and Correlation Functional in DFT

Machine Learning to Improve the Exchange and Correlation Functional in DFT

Institute for Pure & Applied Mathematics (IPAM) via YouTube Direct link

Challenges

19 of 27

19 of 27

Challenges

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Machine Learning to Improve the Exchange and Correlation Functional in DFT

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  1. 1 Introduction
  2. 2 Framework
  3. 3 Motivation
  4. 4 Supercritical liquid
  5. 5 Simulations
  6. 6 State of the art
  7. 7 Adult approach
  8. 8 In real space
  9. 9 Parameters
  10. 10 Projections
  11. 11 Regularization
  12. 12 Basin optimization
  13. 13 Covariance matrix
  14. 14 What we learned
  15. 15 Two methods
  16. 16 Double optimization
  17. 17 Results
  18. 18 Results for water
  19. 19 Challenges
  20. 20 Growth and optimization
  21. 21 Gradient optimization
  22. 22 Consistent loop
  23. 23 Loss function
  24. 24 Enhancement Factors
  25. 25 Energy
  26. 26 Hybrid
  27. 27 DeepMind

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