It gives a perspective on the progress of AI tools in computational chemistry through the lens of his decade-long contributions put in the wider context of the trends in this rapidly expanding field.

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.

MLatom 3 is a program package designed to leverage the power of ML to enhance typical computational chemistry simulations and to create complex workflows.

Chemical bonding group from Xiamen University has developed a hybrid density functional valence bond (VB) method with multistate treatment, namely λ-DFVB(MS).

Recently, Sason Shaik from Hebrew University and coworkers applied the valence bond theory to the study of nine hydrogen bond systems.