A Chemistry-Aware Digital Twin - T3
T3 will create a framework for chemistry-aware digital twins, capable of directly coupling experimental data analytical, computational and machine learning models. A chemistry-aware digital twin will, e.g., be able to optimize the efficiency and durability of discovered materials or Power-to-X systems by coupling both up and down in scale, and thereby accelerating the discovery process.
New catalysts for power-to-x technologies that can meet global demand are a major bottleneck in the green transition. T3 will accelerate the development of efficient and cheaper catalysts, linking the discovered materials to the Power-to-X systems in which they will be applied. The methods for upscaling into systems will be a key competence of T3.
T3 will digitally model the materials discovered in T1 and T2 research themes from the atomic level to the material level and up to the system level in order to see the benefits and consequences of a new material in the Power-to-X process. The chemistry-aware digital twin will be able to track the effects across different scales when a parameter or condition of the material or the power-to-x process is changed.
An effective chemistry-aware digital twin will help us understand where we need to optimize to achieve the best process and discover new ways of operating a Power-to-X process to ensure durability. In addition, it will help to avoid overly complicated models of materials that cannot be simulated.
Modelling physical systems has been a fundamental aspect of my research. The process of simplifying complex systems while retaining essential details, and then using this information to control a process. In recent years, machine learning has emerged as an important new field, opening up a wealth of possibilities. The combination of machine learning and physical based modelling makes this trail particularly exciting and offers numerous opportunities.