skip to content

Materials Modelling Seminar

Professor Michele Ceriotti, Laboratory of Computational Science and Modelling, EPFL

Wednesday 24th April 2019, 12:00

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

Title: Atomistic Machine Learning between Physics and Data


Statistical regression techniques have become very fashionable as a tool to predict the properties of systems at the atomic scale, sidestepping much of the computational cost of first-principles simulations and making it possible to perform simulations that require thorough statistical sampling without compromising on the accuracy of the electronic structure model.

In this talk I will argue how data-driven modelling can be rooted in a mathematically rigorous and physically-motivated framework, and how this is beneficial to the accuracy and the transferability of the model. I will also highlight how machine learning - despite amounting essentially to data interpolation - can provide important physical insights on the behaviour of complex systems, on the synthesizability and on the structure-property relations of materials.

I will give examples concerning all sorts of atomistic systems, from semiconductors to molecular crystals, and properties as diverse as drug-protein interactions, dielectric response of aqueous systems and NMR chemical shielding in the solid state.

Latest news

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...

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.