In silico prediction and screening of modular crystal structures via a high-throughput genomic approach

High-throughput computational methods capable of predicting, evaluating and identifying promising synthetic candidates with desired properties are highly appealing to today's scientists. Despite some successes, in silico design of crystalline materials with complex three-dimensionally extended structures remains challenging. Here we demonstrate the application of a new genomic approach to ABC-6 zeolites, a family of industrially important catalysts whose structures are built from the stacking of modular six-ring layers. The sequences of layer stacking, which we deem the genes of this family, determine the structures and the properties of ABC-6 zeolites. By enumerating these gene-like stacking sequences, we have identified 1,127 most realizable new ABC-6 structures out of 78 groups of 84,292 theoretical ones, and experimentally realized 2 of them. Our genomic approach can extract crucial structural information directly from these gene-like stacking sequences, enabling high-throughput identification of synthetic targets with desired properties among a large number of candidate structures.