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.

Training and using machine learning potentials with MLatom@XACS

MLatom@XACS is a powerful tool for training and using machine learning potentials. It supports a wide variety of representative potentials.

Surface hopping dynamics with MLatom is coming: Join online broadcast!

We will hold online broadcast demonstrating new features on April 3, at 15:30 Beijing time/9:30 am CET on the XACS Youtube channel.

Tutorial on analytical calculation of excited-state energy decomposition with XEDA@XACS

Prof. Su's group has recently developed the GKS-EDA(TD) method, which is based on GKS-EDA, for quantitatively investigating the nature of intermolecular interactions in excited states.

To Page