MLatom 3.10.0

Big improvements for excited-state simulations and surface-hopping molecular dynamics:

• Active learning for surface-hopping dynamics — efficient and robust (often start-to-finish within a couple of days on a single GPU).
• Multi-state learning model (MS-ANI) with unrivaled accuracy for excited-state properties, usable for trajectory surface hopping of multiple molecules.
• gapMD for efficient sampling near conical intersections.

Quality-of-life: visualize molecules and vibrations in Jupyter (mymol.view(normal_mode=1)), view MD trajectories (mytraj.show()) and databases (moldb.show()), and easier molecule loading.

Paper: Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics, ChemRxiv 2024.

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Install: pip install mlatom==3.10.0 · full release notes · documentation