Assessment of the synthetic feasibility of hypothetical zeolite-like materials based on ZeoNet

TL;DR

This paper introduces a CNN-based classifier that effectively predicts the synthetic feasibility of hypothetical zeolite-like structures, outperforming previous methods and identifying promising candidates for experimental synthesis.

Contribution

The study presents a novel four-class CNN classifier using ZeoNet representations that significantly improves accuracy in distinguishing synthesizable zeolites from hypothetical structures.

Findings

Achieves over 99.6% accuracy in classifying structures

Misclassifies only 1,207 out of 330,000 structures

Small subset of misclassified structures are promising synthesis candidates

Abstract

A suite of classifiers was developed to distinguish experimentally synthesized zeolites from computationally predicted zeolite-like structures. Using convolutional neural networks applied to 3D volumetric grids, these classifiers achieve accuracies more than an order of magnitude higher than previous approaches based on geometric filters or other machine learning methods. The best-performing model differentiates among hypothetical zeolites and those that can be synthesized as silicates, as aluminophosphates, or as both. This four-class classifier attains a false negative rate of 3.4% and a false positive rate of 0.4%, misidentifying only 1,207 of over 330,000 hypothetical structures--even though the hypothetical structures exhibit similar formation energies as real zeolites and chemically reasonable bond lengths and angles. We hypothesize that the ZeoNet representation captures essential structural features correlated with synthetic feasibility. In the absence of comprehensive physics-based criteria for synthesizability, the small subset of misclassified hypothetical structures likely represents promising candidates for future experimental synthesis.