AI for smart materials modelling

Materials of the future will rely on nanostructures painstakingly tailored to design constraints and reach unprecedented levels of performance. In order to design these new materials, accurate simulations of material behavior across the scales are needed. Yet, even one of these models currently takes months to run on conventional computers.

The goal of the SLIMM lab is to facilitate virtual material testing across the scales by combining Bayesian machine learning and multiscale solid mechanics into new smart material models and learning frameworks.

Why virtual testing through smart multiscale models?

  • Speed: Designing new materials without waiting for years for simulations to run
  • Accuracy: Precise nanostructure optimization and deeper knowledge on material behavior
  • Sustainability: Fewer destructive experiments needed and lower energy use through faster simulations

The SLIMM Lab is part of the TU Delft AI Labs programme.

Designing new materials and structures requires a deep understanding of material behavior. Bridging these knowledge gaps requires both experiments and advanced computer models

Multiscale computer models of material behavior open up new doors to otherwise inaccessible knowledge of material processes at tiny scales.

Experiments on material samples are carefully crafted in order to extract as much information on material behavior as possible. Machine learning techniques can be used to maximize this information gain.

The team


PhD candidates

Joep Storm

PhD candidate

Associated faculty