Forthcoming Events

05.09.2022 - 09.09.2022, Iseolago hotel, Iseo, Italy.


MUST2022 Conference- a great success!
New scientific highlights- by MUST PIs Wörner, Chergui, and Richardson
FELs of Europe prize for Jeremy Rouxel- “Development or innovative use of advanced instrumentation in the field of FELs”
Ruth Signorell wins Doron prizefor pioneering contributions to the field of fundamental aerosol science
New FAST-Fellow Uwe Thumm at ETH- lectures on Topics in Femto- and Attosecond Science
International Day of Women and Girls in Science- SSPh asked female scientists about their experiences
New scientific highlight- by MUST PIs Milne, Standfuss and Schertler
EU XFEL Young Scientist Award for Camila Bacellar,beamline scientist and group leader of the Alvra endstation at SwissFEL
Prizes for Giulia Mancini and Rebeca Gomez CastilloICO/IUPAP Young Scientist Prize in Optics & Ernst Haber 2021
Nobel Prize in Chemistry awarded to RESOLV Member Benjamin List- for the development of asymmetric organocatalysis
NCCR MUST at Scientifica 2021- Lightning, organic solar cells, and virtual molecules
#NCCRWomen- NCCR MUST celebrates 50 years women’s right to vote in Switzerland

Design of Advanced Materials?

Date Do, 07.11.2019 - Do, 07.11.2019
Time 17:15h
Speaker Prof. Matthew J. Rosseinsky, University of Liverpool, UK
Location EPFL Campus, room BCH 2201
Program Abstract :
The knowledge we have developed through the synthesis and experimental study of extended solids allows us to efficiently identify new materials, in many cases with scientifically interesting or technically important changes in properties. An example is the chemical control of the transparent conducting behaviour of correlated metals (1), evaluated as epitaxial films through optical and transport data. The selection of d0 cations to stabilise oxygen- oxygen bond formation upon deep oxidation of lithium ion cathodes is a further example (2). Here computation provides underpinning guidance in the selection of experimental targets.
However, the large potential range of accessible compositions and structures challenges our present capabilities. As part of the current interest in exploring computationally-enabled routes to new materials, we are developing computational tools for the identification of stable new compositions. We have recently (3) been able to predict ab initio the regions of composition space that afford new materials, and then subsequently isolate those materials experimentally, using the computation of the energies of probe structures identified by new crystal structure prediction methods (4) to explore the space. The presentation will discuss the potential offered by informatics approaches often referred to as machine learning in such work.

(1) J.L. Stoner et al., Advanced Functional Materials 29, 1808609, 2019
(2) Z. Taylor et al., J. Am. Chem. Soc. 141, 7333, 2019
(3) C. Collins et al., Nature 546, 280-284, 2017
(4) C. Collins et al., Faraday Discussions 211, 117, 2018
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