Laboratory of Computational Science and Modeling COSMO
The macroscopic behaviour of materials is determined by the chemical and physical interactions between atoms, averaged over thermal and quantum mechanical fluctuations. At the laboratory of Computational Science and Modelling we simulate matter at the atomic scale, to be able to predict and optimise the properties of materials and molecules starting from fundamental physical principles.
To do so, we need to develop computer simulation methods that are both efficient and accurate, relying on an interdisciplinary expertise that combines understanding of statistical physics, quantum chemistry and materials engineering, with the mathematical and computational tools of data science and machine learning. We then apply these techniques to address materials science problems that are relevant for pressing societal issues – from the metallurgy of lightweight alloys for sustainable transportation, to the determination of the structure and behaviour of hydrogen-bonded materials such as pharmaceutical compounds and bio-inspired polymers.
You can learn more on what we do, and what methods we use, on the Research page, or on the News Archive. If you are interested in joining us, have a look at the Jobs page for open positions and application instructions.
|Head of the laboratory:
Prof. Michele Ceriotti
tel: +41 (0)21 69 32939
Ms. Anne Roy
tel: +41 (0)21 69 32925
|EPFL STI IMX COSMO
MXG 338 (Bâtiment MXG)
Barely porous organic cages for hydrogen isotope separation
Porous materials obtained by assembling cage-shaped molecules of different types achieve high selectivity in separating hydrogen and deuterium gas by means of a quantum sieving process.
Prof. Althorpe gives a seminar on quantum dynamics
Prof. Stuart Althorpe (Cambridge University), who is visiting the Laboratory of Computational Science and Modelling, will give a seminar on "Real-time dynamics from imaginary-time path-integrals: theory and practice" on Tuesday October 29, 16:00, Room MED 2 1124 (CoViz2)
Machine learning the electron density
A collaboration between the Laboratory of Computational Science and Modelling and the Laboratory for Computational Molecular Design developed a transferable and scalable machine-learning model capable of predicting the total electron density directly from the atomic coordinates. This model has been trained on a database of small molecular fragment, and used to predict the electronic density of polypeptides, and to compute electrostatic interactions between fragments, that contribute to the stability of proteins and the interactions between enzymes and drug molecules.