Machine learning has become an integral tool in the theoretical and computational molecular sciences. Uses of machine learning in this area include prediction of molecule and materials properties from large databases of descriptors, design of new molecules with desired characteristics, design of chemical reactions and processes, representation of high-dimensional potential energy and free energy surfaces, creation of new enhanced sampling strategies, and bypassing of costly quantum chemical calculations, to highlight just a few. This lecture will focus on the use of machine learning and rare-event sampling strategies for finding reaction coordinates that characterize transitions between different basins on high-dimensional free energy surfaces and generating pathways between these basins. We will first review collective-variable based enhanced sampling techniques and then compare different classes of machine learning models, including kernel methods, neural networks, decision-tree approaches, and nearest-neighbor schemes for performing the aforementioned tasks. Specific examples from materials science and biomolecules will be used to illustrate the synergistic schemes.
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