Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

Nanoporous materials (NPMs) selectively adsorb and concentrate gases into their pores and thus could be used to store, capture, and sense many different gases. Modularly synthesized classes of NPMs, such as covalent organic frameworks (COFs), offer a large number of candidate structures for each adsorption task. A complete NPM-property table, containing measurements of relevant adsorption properties in candidate NPMs, would enable the matching of NPMs with adsorption tasks. However, in practice, the NPM-property matrix is only partially observed (incomplete); many different properties of many different NPMs have not been measured. The idea in this work is to leverage the observed (NPM, property) values to impute the missing ones. Similarly, commercial recommendation systems impute missing entries in an incomplete product–customer ratings matrix to recommend products to customers. We demonstrate a COF recommendation system to match COFs with adsorption tasks by training a low-rank model of an incomplete COF–adsorption-property matrix constructed from simulated uptakes of CH4, H2O, H2S, Xe, Kr, CO2, N2, O2, and H2 at various conditions. A low-rank model of the COF–adsorption-property matrix, fit to the observed (COF, adsorption property) values, provides (i) predictions of the missing (COF, adsorption property) values and (ii) a “map” of COFs, wherein COFs, represented as points, with similar (dissimilar) adsorption properties congregate (separate). The COF recommendation system is able to rank COFs reasonably well for most of the adsorption properties, but imputation performance diminishes precipitously when the fraction of missing entries exceeds 60%. The concepts in our COF recommendation system can be applied broadly to impute missing data pertaining to many different materials and properties.