Inside the Black Box: A Physical Basis for the Effectiveness of Deep Generative Models of Amorphous Materials

We have recently demonstrated an effective protocol for the simulation of amorphous molecular configurations using the PixelCNN generative model (J. Phys. Chem. Lett. 2020, 11, 20, 8532). The morphological sampling of amorphous materials via such an autoregressive generation protocol sidesteps the high computational costs associated with simulating amorphous materials at scale, enabling practically unlimited structural sampling based on only small-scale experimental or computational training samples. An important question raised but not rigorously addressed in that report was whether this machine learning approach could be considered a physical simulation in the conventional sense. Here we answer this question by detailing the inner workings of the underlying algorithm that we refer to as the Morphological Autoregression Protocol or MAP.