skip to content
 

Materials Modelling Seminar

Professor Kazuto Akagi, Advanced Institute for Materials Research, Tohoku University

Tuesday 6th Mar, 11:00

Goldsmiths 1 (0_017), Department of Materials Science & Metallurgy

Title: Computational Homology and Materials Science

Abstract:

Finding relationship between structure and property is one of the essential subjects in materials science. However, it is still difficult to notice what is the structural motifs or hierarchical information characterizing complex systems. Computational homology based on “persistent homology” [1] is a powerful framework to detect and describe the "shape" in discrete data such as atomic configurations or pixel images. The obtained geometrical information is contracted as a two-dimensional map called “persistence diagram (PD)”, in which birth and death of N-dimensional holes are recorded.

From the view point of materials science, the advantage of this mathematical method is summarized as follows:
(1) Detecting hidden order in the system.
(2) Providing “finger prints (or descriptors)” of complex systems.
(3) Enabling us to treat “inverse problems”.

In this talk, I will introduce the key points of computational homology for materials scientists. After that, we will see how it works in the analysis of molecular dynamics simulations and experimentally observed images, respectively.

[1] Y. Hiraoka et al., PNAS 113, 7035-7040 (2016).

talks.cam: http://talks.cam.ac.uk/talk/index/102214

Latest news

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.

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...