The search for more efficient materials follows a pattern that has changed very little over the years, involving poorly automated phases of synthesis, characterization and measurement of functional properties. This pattern has proved its strength in creating knowledge bases and, in some cases, leading to the discovery of promising formulations. However, this classic approach remains ineffective because it is time-consuming and generally covers a reduced range of compositions.

The project Hiway-2-mat seeks to use high-throughput combinatorial approaches and develop autonomous configurations to explore the compositional spaces of complex oxide materials, with the aim of accelerating the discovery of materials for smart windows, smart sensors, low power lighting and electronic systems. In this context, CALPHAD method appears to be a valuable tool before or during materials exploration, as it can provide a number of useful insights into the role of oxidation state or oxygen partial pressure on phase stability, and on the degree of substitution of doping elements in an oxide matrix.

The aim is to calculate phase diagrams of complex oxides, based on available databases, either to better prepare combinatorial experiments, or to drive the autonomous robot on the fly, providing additional information for on-line characterization.

In this context, you will join the LM2T team within the DIADEM Project Hiway-2-mat (https://www.pepr-diadem.fr/projet/hiway-2-mat/) for innovative materials.

Your role will be to:

1)      Perform thermodynamic simulations using CALPHAD method and Thermo-Calc Software to predict the stability range of a set of complex oxides (i.e. (Ba/Ca/Sr)(Ti/Zr/Sn/Hf)O3, doped VO2 or ZrO2-HfO2-CeO2-SnO2) in different atmosphere conditions (temperature and oxygen partial pressure). In this step, the candidate will also perform a critical review of the thermodynamic data available in the literature.

2)      Develop a rapid screening method to search for the most promising compositions.

3)      Perform thermodynamic modelling assessment of most promising compositions not yet include in the available databases.

The candidate will work closely with the experimental platform development team to guide future trials and adapt the method to better meet large-scale production needs.

Skills/Qualifications:

- PhD degree in Chemistry, Physics, Chemical Engineering, Materials Engineering or related fields.

- You have strong background in thermodynamics.

- Experience with method CALPHAD and Thermocalc is expected, programming skills are essential.