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Physically Inspired Machine Learning for Excited States - IPAM at UCLA
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- 1 Intro
- 2 Phototherapy
- 3 Excited-state surface-hopping dynamics
- 4 Problem: Quantum chemistry (QC)
- 5 Where can machine learning (ML) help?
- 6 Photochemical processes
- 7 Proof of concept
- 8 Training set generation
- 9 Arbitrary phase of the wave function
- 10 ML excited-state dynamics
- 11 Machine learning for photodynamics
- 12 Limitations of existing approach: Phase correction
- 13 Phase-free training algorithm
- 14 Learning nonadiabatic couplings
- 15 Application to tyrosine: Training set
- 16 Roaming in tyrosine
- 17 Unsupervised ML
- 18 Roaming atoms: radicals or protons?
- 19 Summary
- 20 Learning orbital energies
- 21 ML for photoemission spectroscopy
- 22 Generative ML for molecular design
- 23 Targeted molecular design
