Monday 11 September 2017

11th EUCO-TCC - Barcelona

11th European Conference on Theoretical and Computational Chemistry 3rd-7th of September, Barcelona, Spain

The central courtyard of the institute bathed in the glorious Barcelona sun.
Hosted at the Institute for Catalan Studies (IEC), in Barcelona, a majestic building built in the 17th century, the IEC, along with EuCheMS (European Chemical Services), hosted the 11th European Conference on Theoretical and Computational Chemistry.

As a computational chemist working in material science this was the perfect opportunity to network with a diverse group of researchers, who all share the same interest in simulating chemistry at the atomic level. Topics included solvation, catalysis, biochemical reactions, and material science. Given the nature of the conference there was also a focus on the techniques being developed to simulate these systems, such as density functional theory, molecular dynamics, and machine learning.

Highlights for myself included the work by Matti Hellstroem, from Georg-August-Universitat Goettingen, who works for Jorg Behler. Matti presented recent work on simulations of water and sodium hydroxide using the Behler type neural network potentials. The simulations represent how mature these methods are, though also demonstrate that there are still some hurdles to overcome. Mattis’ demonstration of the process of proton transfer through the system, and the importance of the types of clusters that form locally about sodium to enable the making and breaking of bonds.

The SnIP double helix structure
Tom Nilges from the Technical University of Munich, presented his work on the curious inorganic double helix SnIP semiconductor that have potential as a functional material. The double helices consist of  twisted chains of tin iodide (SnI+) intertwined phosphide (P–) chain. The band gap of the material, and mechanical strength, lends itself well to potential solar cell applications, and the next steps are exploring the analogues of the material.

The keynote lecture, and EuChemMS award winner, Ursula Roethlisberger, from Ecole Polytechnique Federale de Lausannae, Institut des Sciences det Ingenierie Chimiques, presented her novel work combining machine learning and computational chemistry. Primarily her work utilises DFT calculations to generate high quality, and well sampled training data, that can then lead to quick hits for discovering molecules with ideal properties. I also took the time to chat to her regarding her work on simulating MALI and related photovoltaic materials, and how she is using machine learning to search the structure space of these materials.

Overall the conference was an excellent opportunity to connect with fellow computational chemists, some who are familiar faces, and to present the research from the SUbST group to a wider audience.