Neural Network Distinguishes Real Crystals from Fantasies express
Porous zeolite minerals work like tiny sponges with holes of a strictly defined size. Through such holes, molecules of one kind pass while others get stuck: this is the basis for water purification, gas separation, and catalysis in petrochemistry. But inventing a new zeolite on paper is much easier than growing it in the lab: dozens of delicate conditions must align for the crystal to form.
The new model neural network looks at the three-dimensional map of atoms in the crystal the way a doctor looks at an X-ray. It compares the pattern with those already seen in real zeolites and delivers a verdict: synthesizable or not. Out of more than three hundred thirty thousand fictional structures, the model mistakenly recognized only about twelve hundred as real: this is dozens of times more accurate than previous methods.
An error of three percent is not a miss but a hint. The very structures that the model took for real, although no one has synthesized them, might actually be feasible. Chemists just need to take this short list and try growing the crystals in an autoclave.
Original abstract
as written by the authors arXiv:2601.17253
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.
Zeolites are even used in cosmetics and as an additive to livestock feed: their porous structure binds toxins and excess moisture.