At COSMO we perform bottom-up atomistic modeling of condensed matter and molecular systems, focusing on method development and statistical sampling. Our goal is to understand the mechanisms that underlie the macroscopic properties of materials and improve them through rational design.

We tackle this challenge through a combination of fundamental science and data-driven approaches. By incorporating physics and chemistry into machine learning methods we build accurate and predictive surrogate models. These models allow us to run reliable simulations that bridge different time and size scales and capture subtle physical effects and complex, large-scale structural features.  We employ these simulations as a tool of discovery: by improving the fidelity of the simulations, we aim to achieve a better understanding of materials’ properties and a reliable description of physical reality. As we believe that research is a collective endeavour, we develop open-source tools for the benefit of other researchers and the larger community.

Using machine learning to predict the properties of materials and molecules.

A path integral simulation of water

Modeling nuclei as quantum particles, to predict the behavior of matter at finite temperature.

Looking for patterns and structure-property relations in complex materials

Bridging length and time scales between atomic-scale phenomena and thermodynamic processes

Modelling sophisticated experiments to understand water and interfaces.

Understanding hydrogen-bonded materials, with atomistic modeling and machine learning