Fig. 2: Comparison of theoretical and ML Models of the Hall-Petch effect.

The success of a given ML model may have little or no relationship to the actual physical processes as the model is merely interpolating between observations. For example, a Gaussian Process model can "capture'' the changeover in the behavior of the flow stress in metals from being dependent on grain boundary density in large-grain metals⁷⁸ to being dominated by grain boundary sliding in nanocrystalline alloys⁷⁹ even though the model is unaware of either mechanism. However, outside the range of acquired data the lack of encoding scientific understanding results in rapidly increasing uncertainties, even in well-calibrated systems. Code for reproducing this figure is available at https://github.com/usnistgov/ml-materials-reflections⁸⁰.