Data-driven materials scouting and inverse design | Dcube


Funding period:Oct. 1, 2023 to Sept. 30, 2027
Agency: DFG
Funding scheme:Research Training Group
Further details:https://tu-dresden.de/ing/forschung/graduiertenkollegs/grk2868

Acknowledgements

We acknowledge funding by the DFG Research Training Group project "Data-driven materials scouting and inverse design" (Dcube, grant agreement ID: GRK 2868)


Description

The objective of this project is the identification and application-tailored design of alloys which optimally match mechanical and thermodynamic require- ments, bearing the potential to outperform known materials and open new perspectives for target advanced applications. Hereby, nanoscale phenomena such as the quan- tum-mechanical binding between atoms leading to the formation of atomistic lattices dictate bulk material properties including density, mechanical stiffness and thermodynamic behavior. Thus, a detailed understanding of the interplay between atomistic decomposition and material properties is essential for an inverse data-driven design of materials with desired properties.

Data-driven materials scouting and inverse design | Dcube


Funding period:Oct. 1, 2023 to Sept. 30, 2027
Agency: DFG
Funding scheme:Research Training Group
Further details:https://tu-dresden.de/ing/forschung/graduiertenkollegs/grk2868

Acknowledgements

We acknowledge funding by the DFG Research Training Group project "Data-driven materials scouting and inverse design" (Dcube, grant agreement ID: GRK 2868)


Description

The objective of this project is the identification and application-tailored design of alloys which optimally match mechanical and thermodynamic require- ments, bearing the potential to outperform known materials and open new perspectives for target advanced applications. Hereby, nanoscale phenomena such as the quan- tum-mechanical binding between atoms leading to the formation of atomistic lattices dictate bulk material properties including density, mechanical stiffness and thermodynamic behavior. Thus, a detailed understanding of the interplay between atomistic decomposition and material properties is essential for an inverse data-driven design of materials with desired properties.