Is novelty predictable?

Understand

Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal implications spanning drug development and manufacturing, plastic degradation, and carbon sequestration.

• When designing objects to achieve novel property values with machine learning, one faces a fundamental challenge: how to push past the frontier of current knowledge, distilled from the training data into the model, in a manner that rationally controls the risk of failure.
• If one trusts learned models too much in extrapolation, one is likely to design rubbish.
• In contrast, if one does not extrapolate, one cannot find novelty.