In recent decades, data-driven methodologies have emerged as irreplaceable tools in material science, particularly for elucidating structure-property relationships and facilitating the discovery of novel materials. However, despite the rapid development witnessed in other domains, amorphous materials have received relatively less attention in this context. The disordered atomic structure of amorphous materials resulting from irreversible reactions between building blocks has posed a difficulty in structural modeling, leading to a lack of databases that accurately reflect the amorphous nature of these materials. In this work, a database composed of 10,237 porous polymer networks (PPNs) was constructed from self-assembly simulations, resulting in the largest database of PPNs considering their amorphous characteristics. Through the distinct differences observed in comparison with existing databases, we emphasize that carefully considering the structural disorder of PPNs is essential for accurately characterizing their chemical behaviors. Machine learning models trained on the constructed database have confirmed that the macroscopic properties of amorphous PPNs can be predicted solely from the atomic structures of their monomers, implying that the characteristics of previously unseen PPNs can be assessed without the need for additional self-assembly simulations.
Tetrahderal PPN monomers were prepared based on commercially available dibromide linkers. Amorphous PPN Database was constructed through self-assembly simulations of the prepared monomers, where the irreversible bond formation between monomers were modeled via distance-based bond formation algorithm.
High-throughput screening was conducted on the constructed database, and PPNs with exceptional methane capacities were verified. The high-performing PPN structures exhibit high methane working capacities, which are even comparable to those of representative MOFs. Notably, PPNs are shown to be more competitive in terms of gravimetric units, making them an attractive class for reducing the mass of the adsoprtion tank.
Machine learning models were trained to predict the macroscopic properties of PPNs in the amorphous state. Various input representations were tested and satisfactorily high accuracy was achieved. Further analysis on the trained model revealed that the topological surface area (TPSA) of monomers plays an crucial role in determining the porosity of amorhpous PPNs.
