High-Throughput Screening Approach for Nanoporous Materials Genome Using Topological Data Analysis: Application to Zeolites

Abstract

The materials genome initiative has led to the creation of a large (over a million) database of different classes of nanoporous materials. As the number of hypothetical materials that can, in principle, be experimentally synthesized is infinite, a bottleneck in the use of these databases for the discovery of novel materials is the lack of efficient computational tools to analyze them. Current approaches use brute-force molecular simulations to generate thermodynamic data needed to predict the performance of these materials in different applications, but this approach is limited to the analysis of tens of thousands of structures due to computational intractability. As such, it is conceivable and even likely that the best nanoporous materials for any given application have yet to be discovered both experimentally and theoretically. In this article, we seek a computational approach to tackle this issue by transitioning away from brute-force characterization to high-throughput screening methods based on big-data analysis, using the zeolite database as an example. For identifying and comparing zeolites, we used a topological data analysis-based descriptor (TD) recognizing pore shapes. For methane storage and carbon capture applications, our analyses seeking pairs of highly similar zeolites discovered good correlations between performance properties of a seed zeolite and the corresponding pair, which demonstrates the capability of TD to predict performance properties. It was also shown that when some top zeolites are known, TD can be used to detect other high-performing materials as their neighbors with high probability. Finally, we performed high-throughput screening of zeolites based on TD. For methane storage (or carbon capture) applications, the promising sets from our screenings contained high-percentages of top-performing zeolites: 45% (or 23%) of the top 1% zeolites in the entire set. This result shows that our screening approach using TD is highly efficient in finding high-performing materials. We expect that this approach could easily be extended to other applications by simply adjusting one parameter, the size of the target gas molecule.

Understanding the diversity of the metal-organic framework ecosystem

“…At present, different strategies have been developed to represent MOFs with feature vectors [9][10][11][12] . However, the global material descriptors 9,[13][14][15][16] that are presently used are not ideal for our purpose. We would like to directly connect to the structural building blocks of MOFs, which closely resemble the chemical intuition of MOF chemists, in which a MOF is a combination of the pore geometry and chemistry (i.e., metal nodes, ligands and functional groups) 6,17 .…”

Representation of Molecular Structures with Persistent Homology Leads to the Discovery of Molecular Groups with Enhanced CO2 Binding

“…These homological features are summarized in a persistence diagram (PD). [60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76][77][78] A PD encodes molecular features such as bonds and rings. The PDs can then be vectorized into a persistence image 79 (PI) for use as a molecular representation.…”

Geometric landscapes for material discovery within energy–structure–function maps

“…47 The persistent homology barcodes then were computed over ltering topological objects to the size of 8 A of the constructed Vietoris-Rips complexes 48 up to the second dimension for the sampled point cloud (see Fig. 1, Method section, and our previous works 35,49 for more details). Each dimension of the barcode captures part of the topological features of the pore shape.…”