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Materials Theory Group

 

Stochastic Hyperspace Embedding And Projection

Stochastic Hyperspace Embedding And Projection (SHEAP) is a dimensionality reduction method designed for visualising potential energy surfaces.

Computational structure prediction can assist the discovery of new materials. One searches for the most stable configurations of a given set of atomic building blocks, which correspond to the deepest regions of an energy landscape—the system's energy as a function of the relative positions of its atoms. To explore these landscapes efficiently, it is important to understand their topologies. However, they exist in spaces with very large numbers of dimensions, making them difficult to visualise. SHEAP uses dimensionality reduction through manifold learning to effectively visualise the distribution of stable structures across a high-dimensional energy landscape.

 

Latest news

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Congratulations Ben Shires!

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Ben completed his PhD viva last week, covering his work on SHEAP , and he will soon be Dr Shires. Congratulations! shires.jpg

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Postdoc in High Temperature Conventional Superconductivity

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Applications are invited for a postdoctoral research position with Professor Chris Pickard at the University of Cambridge. Recent advances in computational methods have raised the prospect of the in silico design of high...

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