Open framework silicides and analogous: from Machine Learning-guided Phases to high-pressure synthesis – Openbar

The current energy crisis requires the development of sustainable and eco-friendly energy sources. However, energy storage and conversion devices play a crucial role in the use of renewable energy solutions for achieving an efficient reduction of carbon dioxide emissions. Besides primary electrical energy production from solar and wind power, thermoelectric generators offer promising solutions for electricity production by harnessing wasted heat produced from industry. Enhancing their efficiencies and utilising abundant, non-toxic, and inexpensive materials will determine their success in the coming years. The search for new energy materials using plentiful elements is of great interest, notably by investigating intermetallic silicides and new Si allotropes with open framework crystal structure such as clathrates compounds and compounds with channels. Both in the case of thermoelectric and photovoltaic applications, it is necessary to have semiconducting compounds.
The aims of the Openbar project are to investigate and analyse quenchable metastable materials using a combined experimental and theoretical approach, with the aim of obtaining new semiconducting open framework silicides or Si allotropes, as well as their analogous M-X (M = Na, K et X = Si, Ge, Sn), for energy applications. In silico Crystal Structure Prediction (CSP), together with machine-learned potentials will guide experimentalists in the search for new materials under High Pressure and High Temperature (HP-HT) conditions. The thermoelectric and spectroscopic properties as well as the Na diffusion will be studied for the evaluation of their potential in energy applications.