Materials research relies on intuition-driven exploration, where chemists and engineers tweak tiny elements in compositions or processes and then wait to see the result. This approach is powerful - but slow.
Add to this the millions of variables and possibilities in chemistry, structure, and processing, and it is little wonder R&D takes several years to decades to produce successful outcomes.1
AI and machine learning stand to transform this process by:
- Learning structure-property relationships from available datasets and predicting which new materials will match target properties
- Enabling inverse design by starting from a desired characteristic or performance and searching for suitable compositions and microstructures
- Integrating with simulations and experiments to create active-learning loops that select each new measurement for maximum information gain.1
So how does this work in practice?
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