skip to content

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

New carbonates uncovered

7 January 2022

A study by Joseph Nelson and Chris Pickard of the Department of Materials Science and Metallurgy, University of Cambridge and the AIMR, Tohoku University, uses structure prediction to exhaustively explore the Ti-C-O and Al-C-O ternary systems.

Postdoc in High Temperature Conventional Superconductivity

5 January 2022

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...

Visualising potential energy surfaces using dimensionality reduction

25 November 2021

Computational structure prediction has emerged as a highly successful approach to the discovery of new materials. Candidate structures are created by constructing the most stable configurations that can be adopted by a given...

The elements of life under pressure

1 July 2021

First-principles structure prediction sheds light on high-pressure compounds formed from carbon, hydrogen, nitrogen and oxygen.

AIRSS for battery cathode materials

15 June 2021

A team of researchers at Cambridge and University College London have developed a computational framework for battery cathode exploration based on ab initio random structure searching.

Anatase-like Grain Boundary Structure in Rutile Titanium Dioxide

30 April 2021

A collaboration between researchers at Cambridge and AIMR has shed light on grain boundary structures in titania.

Physics World Breakthrough of the Year finalists for 2020

17 December 2020

A paper coauthored by Chris Pickard and Bartomeu Monserrat has been selected as a Top 10 finalist in Physics World's breakthroughs of the year for 2020.