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

Area of Science:

• Materials Science
• Computational Chemistry
• Data Science

Background:

• The Materials Genome Initiative has generated vast databases of nanoporous materials.
• Discovering novel materials is hindered by the lack of efficient computational analysis tools.
• Current brute-force simulation methods are computationally intractable for large-scale screening.

Purpose of the Study:

• To develop an efficient computational approach for analyzing large nanoporous material databases.
• To transition from brute-force methods to high-throughput screening using big-data analysis.
• To demonstrate the efficacy of a topological data analysis-based descriptor (TD) for material property prediction.

Main Methods:

• Utilized a topological data analysis (TD) descriptor to identify and compare zeolite pore shapes.
• Applied TD to analyze the zeolite database for methane storage and carbon capture applications.
• Performed high-throughput screening of zeolites using TD to identify promising candidates.

Main Results:

• TD successfully identified correlations between zeolite similarity and performance properties.
• TD enabled the prediction of performance properties for similar zeolites.
• High-throughput screening using TD identified sets with a high percentage of top-performing zeolites (45% for methane storage, 23% for carbon capture).

Conclusions:

• The developed TD-based screening approach is highly efficient for discovering high-performing nanoporous materials.
• This method can be extended to various applications by adjusting a single parameter (target gas molecule size).
• The findings pave the way for accelerated discovery of novel materials for energy and environmental applications.