Join online broadcast: Active learning for building your data and machine learning potentials

In the broadcast, we will demonstrate how MLatom@XACS can be used for accelerating expensive quantum chemical simulations via efficient building of robust machine learning potentials.

JPCL | Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training

See our paper in JPCL for more details as well as the tutorials on how to use MLatom for such simulations.

April updates: Backup, feedback, surface-hopping dynamics, constrained geometry optimization, and more

We have made several updates of the XACS platform and software.

Faster & more accurate than DFT: AIQM1 in MLatom@XACS

AIQM1 (artificial intelligence–quantum mechanical method 1)

Inorg. Chem. : Application of chemical bond analysis in lithium-sulfur batteries design

New example on the application of XACS showcases the study of Wang Changwei from Shaanxi Normal University and Mo Yirong from the University of North Carolina at Greensboro.

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