Alloy Design Foundry
The Alloy Design Foundry provides a suite of analytical tools which quickly navigate the massive material and information spaces. These tools are unique in that they integrate considerations of both underlying physics and materials science with the complexity of data science. Through exploration of the chemistry-processing-performance envelope, we have developed a toolset looking at all aspects of chemical design.
- Natural Language Processing : Capture chemistry-processing-performance relationships hidden in the large amounts of legacy information.
- Case Study on Alloy Design : Tracking the role of chemistry on multiscale engineering properties (eg. environmental degradation)
- Manifold Learning : Identify new unexplored systems through exploration of chemistry-performance relationships in new systems.
- Case Study on Alloy Design : Searching for substitutions for high temperature properties with manifold learning to track similarities
- Graphics Recognition : Find new material chemistries with target behavior by extracting geometrical features in phase diagrams.
- Set Theory : Select most promising materials through consideration of chemistry-structure-performance trade-offs in sparse and uncertain data.
- Data driven microscopy : Extract chemical and microstructural details hidden in large amounts of point cloud and spectral experimental data.
- Theory Guided Microscopy
- Automated Clustering Analysis