TCM
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Andrew Morris

Dr Andrew Morris

Dr Andrew Morris

Winton Advanced Research Fellow

Phone: +44(0)1223 7 47375
Email: ajm255 @ cam.ac.uk
Personal web site

TCM Group, Cavendish Laboratory
19 JJ Thomson Avenue,
Cambridge, CB3 0HE UK.

Research Group


Also:

Office: 2.90
The Maxwell Centre
JJ Thomson Avenue,
Cambridge.

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Research

I am a Winton Advanced Research Fellow in the Cavendish Laboratory at The University of Cambridge. I am associated with both Nanoscience Centre and the Theory of Condensed Matter group.

My current interest is in applying the AIRSS method to a range of different materials science problems, focussing mainly on lithium-ion batteries. "Trial and error" plays a large part in the discovery of new materials. From the initial idea, the material must be synthesised and categorised before it can tested which is slow, difficult and expensive. High-throughput computation accelerates this process by suggesting then screening new materials, allowing us to ask "what if?" without the time and expense of manufacturing and categorizing samples. I model Li-ion batteries at the atomic level and try to uncover new materials to increase their capacity.

I use global search techniques such as ab initio random structure searching (AIRSS) to predict the ground-state structure of materials. From the ground state we use theoretical spectroscopy techniques to compare our results to experiment. As a junior developer of the electronic structure code CASTEP I develop tools for optics, electron-energy loss spectroscopy (EELS) and core-loss analysis through the OptaDOS code. I use and modify CASTEP-NMR to calculate the chemical shielding of battery materials in collaboration with experimentalists.

I am based in the Physics Department with strong links to the Chemistry Department and am currently looking for PhD and Part III project students.

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In Plain English

I use theoretical methods to predict the behaviour of materials. . "Trial and error" plays a large part in the discovery of new materials. From the initial idea, the material must be synthesised and categorised before it can tested which is slow, difficult and expensive. High-throughput computation accelerates this process by suggesting then screening new materials, allowing us to ask "what if?" without the time and expense of manufacturing and categorizing samples. Currently I am interested in predicting the atomic structure of batteries. Which will then allow us to fully exploit their potential.