Overfitting in nanotechnology often manifests when predictive models are developed using limited datasets. For instance, if a model is trained on experimental data from a small number of synthesis processes or characterization techniques, it may capture specific quirks of the data rather than the general trends. This can lead to inaccurate predictions when applied to new, unseen data.
