Optimize molecular geometries easier with MLatom 3.4.0

Released on 29.04.2024.

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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.

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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.

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

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

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

AIQM1 (artificial intelligence–quantum mechanical method 1)

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