Machine Learning for Nonadiabatic Molecular Dynamics: Best Practices and Recent Progress

Key Points

• Understanding molecular excited states revolutionizes design frameworks in functional molecules.
• Critical findings highlight the role of machine learning in enhancing predictions of photophysical properties.
• Innovative practices for nonadiabatic molecular dynamics showcase the growing intersection of AI with chemistry.
• Application of machine learning in organic chemistry and chemical biology opens new pathways for research.