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

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

In this tutorial, we show how to perform such simulations with the newly released MLatom 3.5.0 on an example reproducing the above PNAS paper.