Visualising energy landscapes using stochastic neighbour embedding

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• Ben Shires
• Monday 25 November 2019, 16:30-17:00
• Mott Seminar (531) room, top floor of the Mott Building, in the Cavendish Laboratory, West Cambridge..

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.

This talk is included in these lists:

• Hanchen DaDaDash
• Lennard-Jones Centre external
• Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
• Mott Seminar (531) room, top floor of the Mott Building, in the Cavendish Laboratory, West Cambridge.

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