Clustering Molecular Energy Landscapes by Adaptive Network Embedding
Paula Mercurio Di Liu
In order to efficiently explore the chemical space of all possible small
molecules, a common approach is to compress the dimension of the system to
facilitate downstream machine learning tasks. Towards this end, we present a
data driven approach for clustering potential energy landscapes of molecular
structures by applying recently developed Network Embedding techniques, to
obtain latent variables defined through the embedding function. To scale up the
method, we also incorporate an entropy sensitive adaptive scheme for
hierarchical sampling of the energy landscape, based on Metadynamics and
Transition Path Theory. By taking into account the kinetic information implied
by a system's energy landscape, we are able to interpret dynamical node-node
relationships in reduced dimensions. We demonstrate the framework through
Lennard-Jones (LJ) clusters and a human DNA sequence.