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Visualising energy landscapes using stochastic neighbour embedding

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If you have a question about this talk, please contact Bingqing Cheng .

We present SHEAP - Stochastic Hyperspace Embedding And Projection – a tool designed to process the structural data obtained from a materials structure search, in order to produce a visualisation of the energy surface being sampled. We have drawn inspiration from state-of-the-art algorithms for dimensionality reduction of high-dimensional data, such as t-SNE and UMAP . We illustrate the power of SHEAP through its application to the model energy landscapes defined by systems of particles interacting via a simple Lennard-Jones pair potential.

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

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