skip to content

Materials Theory Group

 

Research Assistant/Associate (Fixed Term) - Algorithms to navigate material structure space

Department/Location: Department of Materials Science and Metallurgy, West Cambridge Site

Salary: £25,728-£38,833

Reference: LJ15801

Closing date: 22 July 2018

University of Cambridge, Department of Materials Sciences & Metallurgy, United Kingdom and Tohoku University, Advanced Institute for Materials Research (AIMR), Japan

Algorithms to navigate material structure space

A post-doctoral fellowship is offered to take part in a project to explore the development of novel algorithms to manage and extract meaning from large quantities of computational materials data. As computational materials discovery moves beyond the consideration of existing materials data to a broad search of configurational space, the quantity of data generated is overwhelming. Structure prediction techniques (such as Ab Initio Random Structure Searching - Pickard and Needs, JPCM, 23 (5), 053201, 2011) produce tens of million local minima. Each of these local minima requires a full, first principles, structural optimisation, and the consideration of several hundred configurations. There is already a need to manage and explore billions of structures.

Profs Akagi and Pickard have explored the application of persistent homology (Hiraoka et al.,PNAS, 113, 7035-7040, 2016) and complex network theory (Ahnert, Grant and Pickard, npj Computational Materials, 2017) to the analysis of materials structure data. It is expected the project will critically examine these approaches, propose and develop alternative algorithms.

The role consists in the following duties:
- to develop, implement, and critically assess novel algorithms to manage and extract meaning from large quantities of materials structure data
- to apply these novel algorithms to challenging materials problems
- to analyse and appropriately manage and disseminate generated data
- to promptly write up results for publication
- to visit and host visitors to/from Tohoku University as required, and to prepare joint workshops.

This post is suitable for a candidate who has or is about to obtain a PhD in the computational aspects of Condensed Matter Physics, Materials Science or Chemistry, with a strong mathematical background.

This is a joint project between the Materials Theory Group, Department of Materials Science and Metallurgy, University of Cambridge, and the Advanced Institute for Materials Research (AIMR), Tohoku University. The project is central to the AIMR's mission to integrate mathematics and the materials sciences, and while the position will be based at the University of Cambridge, the candidate will be expected to play an active role in the development and promotion of the AIMR-Cambridge partnership. Example activities include the preparation of joint workshops, and extended research visits to Tohoku University.

Fixed-term: The funds for this post are available for 3 years in the first instance, subject to probation.

Starting date: position available immediately.

To apply online for this vacancy and to view further information about the role, please visit: http://www.jobs.cam.ac.uk/job/17766.

Latest news

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...

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...

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...

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...

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...

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.