High-Throughput Screening Approach for Nanoporous Materials Genome Using Topological Data Analysis: Application to Zeolites
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- Institut des Sciences et Ingéniere Chimiques, Valais, Ecole Polytechnique Fédérale de Lausanne (EPFL), Rue de l’Industrie 17, CH-1951 Sion, Switzerland, School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
- Institut des Sciences et Ingéniere Chimiques, Valais, Ecole Polytechnique Fédérale de Lausanne (EPFL), Rue de l’Industrie 17, CH-1951 Sion, Switzerland
- Department of Mathematics and Swansea Academy of Advanced Computing, Swansea University, Singleton Park, Swansea SA28PP, United Kingdom
- SV BMI UPHESS, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
- Institut des Sciences et Ingéniere Chimiques, Valais, Ecole Polytechnique Fédérale de Lausanne (EPFL), Rue de l’Industrie 17, CH-1951 Sion, Switzerland, Department of Chemical and Biomolecular Engineering, University of California at Berkeley, Berkeley, California 94720, United States
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 highperforming 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.
- Research Organization:
- Energy Frontier Research Centers (EFRC) (United States). Center for Gas Separations Relevant to Clean Energy Technologies (CGS); ShanghaiTech Univ., Shanghai (China)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES)
- Grant/Contract Number:
- SC0001015
- OSTI ID:
- 1462132
- Alternate ID(s):
- OSTI ID: 1508600
- Journal Information:
- Journal of Chemical Theory and Computation, Journal Name: Journal of Chemical Theory and Computation Vol. 14 Journal Issue: 8; ISSN 1549-9618
- Publisher:
- American Chemical SocietyCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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