University of Cambridge > Talks.cam > Machine learning in Physics, Chemistry and Materials discussion group > Visualising energy landscapes using stochastic neighbour embedding

Visualising energy landscapes using stochastic neighbour embedding

Add to your list(s) Download to your calendar using vCal

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 series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2020 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity