Simulations of Disordered Matter in 3D with the Morphological Autoregressive Protocol (MAP) and Convolutional Neural Networks

Disordered molecular systems such as amorphous catalysts, organic thin films, electrolyte solutions, and water are at the cutting edge of computational exploration today. Traditional simulations of such systems at length-scales relevant to experiments in practice require a compromise between model accuracy and quality of sampling. To remedy the situation, we have developed an approach based on generative machine learning called the Morphological Autoregressive Protocol (MAP) which provides computational access to mesoscale disordered molecular configurations at linear cost at generation for materials in which structural correlations decay sufficiently rapidly. The algorithm is implemented using an augmented PixelCNN deep learning architecture that we previously demonstrated produces excellent results in 2 dimensions (2D) for mono-elemental molecular systems. Here, we extend our implementation to multielemental 3D and demonstrate performance using water as our test system in two scenarios: 1. liquid water, and 2. a sample conditioned on the presence of a rare motif. We trained the model on small-scale samples of liquid water produced using path-integral molecular dynamics simulation including nuclear quantum effects under ambient conditions. MAP-generated water configurations are shown to accurately reproduce the properties of the training set and to produce stable trajectories when used as initial conditions in classical and quantum dynamical simulations. We expect our approach to perform equally well on other disordered molecular systems while offering unique advantages in situations when the disorder is quenched rather than equilibrated

Influence of different ester side groups in polymers on the vapor phase infiltration with trimethyl aluminum

The vapor phase infiltration (VPI) process of trimethyl aluminum (TMA) into poly(4-acetoxystyrene) (POAcSt), poly(nonyl methacrylate) (PNMA) and poly(tert-butyl methacrylate) (PtBMA) is reported. Depth-profiling X-ray photoelectron spectroscopy (XPS) measurements are used for the first time for VPI based hybrid materials to determine the aluminum content over the polymer film thickness. An understanding of the reaction mechanism on the interaction of TMA infiltrating into the different polymers was obtained through infrared (IR) spectroscopy supported by density functional theory (DFT) studies. It is shown that the loading with aluminum is highly dependent on the respective ester side group of the used polymer, which is observed to be the reactive site for TMA during the infiltration. IR spectroscopy hints that the infiltration is incomplete for POAcSt and PNMA, as indicated by the characteristic vibration bands of the aluminum coordination to the carbonyl groups within the polymers. In this context, two different reaction pathways are discussed. One deals with the formation of an acetal, the other is characterized by the release of a leaving group. Both were found to be in direct concurrence dependent on the polymer side group as revealed by DFT calculations of the IR spectra, as well as the reaction energies of two possible reaction paths. From this study, one can infer that the degree of infiltration in a VPI process strongly depends on the polymer side groups, which facilitates the choice of the polymer for targeted applications