Ranking the Synthesizability of Hypothetical Zeolites with the Sorting Hat

TL;DR

This paper introduces a data science approach called the 'zeolite sorting hat' that classifies and ranks hypothetical zeolite structures based on their likelihood of being synthesizable, aiding in the discovery of new materials.

Contribution

The study presents a novel classification method with high accuracy for identifying promising zeolite candidates from large databases, incorporating structural features and stability estimates.

Findings

Achieved 95% accuracy in classifying real vs. theoretical zeolites.

Identified key structural features influencing synthesizability.

Provided a ranking system for promising hypothetical frameworks.

Abstract

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of distinct siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme-the "zeolite sorting hat"-that exploits interatomic correlations to produce a 95% real versus theoretical zeolites classification accuracy. The hypothetical frameworks that are grouped together with known zeolites are promising candidates for synthesis, that can be further ranked by estimating their thermodynamic stability. A critical analysis of the classifier reveals the decisive structural features. Further partitioning into compositional classes provides guidance in the design of synthetic strategies.