Machine learning plays an important role in the discovery and deployment of NPMs . Supervised machine learning models have been widely used to predict the adsorption properties of NPMs from vectors of hand-crafted structural features or from a graph representation . Unsupervised machine learning methods have been used to embed NPMs into a lowdimensional "material space" and cluster together NPMs with similar structures . Genetic algorithms and Monte Carlo tree search have been used to more efficiently search for the NPM(s) with an optimal adsorption property. Finally, recently an autoencoder enabled inverse design of NPMs, where one specifies a desired adsorption property, and the machine learning model generates a NPM structure with that property. To enable machine learning approaches to NPM discovery, several open, structured databases of (i) crystal structure models of NPMs , (ii) simulated and experimentally measured adsorption properties of NPMs, and (iii) electronic properties of NPMs , have been curated. Text mining and natural language processing could be used to extract data and knowledge from the literature for machine learning studies as well .