Machine Learning for Non-adiabatic Molecular Dynamics

Curious about how machine learning is transforming excited-state molecular dynamics?

Our latest overview dives into best practices for applying machine learning in non-adiabatic dynamics simulations, from data pre-processing and surface fitting to post-simulation analysis. Learn how machine learning approaches can help overcome computational bottlenecks and uncover patterns in complex photochemical systems — paving the way for data-driven design of photochemistry and -physics.

Check out our best practices article:

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