Exploring the robust extrapolation of high-dimensional machine learning potentials

We show that, contrary to popular assumptions, predictions from machine learning potentials built upon high-dimensional atom-density representations almost exclusively occur in regions of the representation space which lie outside convex hull defined by training set points. then propose a perspective rationalize domain robust extrapolation and accurate prediction atomistic terms probability density induced points