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.

DFT calculations online on XACS cloud

Want to run DFT calculations in an easy way? Search no more!

Transfer learning for better AI models with less data

Transfer learning (TL) is an often-used technique in machine learning that helps you train better AI models.

Nat. Commun.: Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by AIQM1

Recently, the Chung group at Southern University of Science and Technology (SUSTech) has combined efficient machine learning potentials (MLPs) with multi-scale quantum refinement methods to enhance computational efficiency and reliability.

Easy-to-use universal AI models for modern computational chemistry

MLatom supports a wide range of universal machine learning (ML)-based models including ML potentials and hybrid ML-enhanced quantum mechanical (QM) methods.

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