Editor's note: Prof. Pavlo O. Dral's review ‘ AI in computational chemistry through the lens of a decade-long journey ’ was published open access as an invited Feature Article in Chemical Communication. It gives a perspective on the progress of AI tools in computational chemistry through the lens of his decade-long contributions put in the wider context of the trends in this rapidly expanding field.

After reading the review, you will learn about:

• why you should use ML-improved QM methods whenever possible (e.g.,  AIQM1  instead of B3LYP for neutral, closed-shell CHNO-containing molecules)

• the power of  Δ-learning  providing a robust solution for integrating ML with QM and …

how it is related to transfer learning

how it can be generalized to learning from multiple levels of QM methods ( hierarchical ML )

why you should use the term hierarchical ML and not Δ-learning when your baseline is also ML potential

• the  zoo of machine learning potentials

• software for AI-enhanced computational chemistry  and democratizing these calculations through the  XACS cloud computing

• running faster  ground – and  excited -state dynamics,  UV/vis  and  two-photon  absorption spectra

• how AI enables fundamentally new ways of simulations, e.g., by  directly learning MD trajectories

• ChatGPT and other elephants in the room

• and much more.

If you want to learn more about the research please visit the group's website dr-dral.com

Paper:

AI in computational chemistry through the lens of a decade-long journey . Chem. Commun. 2024, Advance Article. (open access under the CC-BY license)
Pavlo O. Dral*