Highlights

Arrows connect a picture of an atom, an artificial neural network, a crystal structure and a pressure-temperature phase diagram. The phase diagram contains liquid, cubic and hexagonal regions as determined by calculations in agreement experimental points.

Fast and easy exploration of crystal properties using machine-learned Ephemeral Data-derived Potentials

12 January 2024

Machine learning is quickly gaining prominence in the field of computational materials science. In the Materials Theory Group, we develop so-called ‘machine learned interatomic potentials’ (MLIPs), which can describe the interactions between atoms in both solids and liquids. MLIPs are trained on vast quantities of data...

Several points representing lutetium, nitrogen and hydrogen compounds. One of which has photorealistic renderings at a range of pressures from 0 to 800kbar. Increasing the pressure changes the colour from blue to pink.

Structure and colour in nitrogen-doped lutetium hydride

19 December 2023

Superconducting materials have a wide range of applications - from efficient power transmission to the advanced electromagnetics used in MRI machines - due to their loss-free conductivity. Current practical superconductors only exhibit these remarkable properties at temperatures below the so-called critical temperature T c...

Quantum-induced hydrogen hopping in high-temperature superconducting lanthanum polyhydride

14 April 2023

Figure caption : Quantum effects are essential for hydrogen to dynamically explore different configurations. On the left, we see how the hydrogen atoms cover much larger distances at all temperatures when quantum effects are taken into account. On the right, we see that this reflects itself in a much larger spread of...

Flat water and ice

26 September 2022

Figure Caption : Pentagonal ice – a two-dimensional form of ice predicted to form when water is squeezed between graphene sheets. Water can be found trapped in nanoscale cavities, for example in biological membranes, or in minerals. This nanoconfined water behaves very differently to the water we use in everyday life, but...

Congratulations Ben Shires!

2 August 2022

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

Quicker Crystals

14 July 2022

First-principles structure prediction has enabled the computational discovery of materials with extreme, or exotic properties. For example, the dense hydrides, which following computational searches have been found to exhibit high-T c superconductivity. However, the quantum mechanical calculations that are performed are...

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 temperature conventional superconductors. This project aims to build on these advances and accelerate...

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 set of atomic building blocks. This corresponds to finding the deepest regions of an energy...

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.