Active learning for building data-efficient machine learning potentials

We have recently developed the physics-informed active learning protocol for efficient data sampling and training potentials from scratch as described in this preprint.

Supercharge your computational chemistry with the universal and updatable AI models

we provide the platform with universal and updatable AI models – UAIQM for short.

Course “Modern computational chemistry and AI” by Pavlo O. Dral

This mini-course gives a practical introduction into computational chemistry and artificial intelligence (AI).

Molecular IR spectra simulations online!

Molecular IR spectra can now be calculated online with MLatom@XACS with DFT using a simple input file.

JCTC: Surface hopping dynamics with QM and ML methods

XACS team in collaboration with Mario Barbatti and groups in Warsaw University and Zhejiang lab has recently published a paper in JCTC about the versatile Python implementation of surface-hopping dynamics.

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