ML of excited states

There are several ways to learn and predict excited-state energies and forces with MLatom. You can train a single model for each state or use our MS-ANI to learn all electronic state energies and forces simultaneously. It is recommended that you get familiar with excited-state data format in MLatom which are required for training ML models. See the manuals for other cases such as application of ML for UV/vis one- and two-photon absorption calculations.

Multi-state ANI models

Multi-state learning model (MS-ANI) that has unrivaled accuracy for excited state properties (accuracy is often better than for models targeting only ground state!). We demonstrate that this model can be used for trajectory-surface hopping of multiple molecules (not just for a single molecule!) in:

  • Mikołaj Martyka, Lina Zhang, Fuchun Ge, Yi-Fan Hou, Joanna Jankowska, Mario Barbatti, Pavlo O. Dral. Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning. 2024. Preprint on ChemRxiv: https://doi.org/10.26434/chemrxiv-2024-dtc1w.

Zip with tutorial materials including Jupyter notebook:

msani