Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances

by   Daniel Schwalbe-Koda, et al.

Zeolites are inorganic materials known for their diversity of applications, synthesis conditions, and resulting polymorphs. Although their synthesis is controlled both by inorganic and organic synthesis conditions, computational studies of zeolite synthesis have focused mostly on organic template design. In this work, we use a strong distance metric between crystal structures and machine learning (ML) to create inorganic synthesis maps in zeolites. Starting with 253 known zeolites, we show how the continuous distances between frameworks reproduce inorganic synthesis conditions from the literature without using labels such as building units. An unsupervised learning analysis shows that neighboring zeolites according to our metric often share similar inorganic synthesis conditions, even in template-based routes. In combination with ML classifiers, we find synthesis-structure relationships for 14 common inorganic conditions in zeolites, namely Al, B, Be, Ca, Co, F, Ga, Ge, K, Mg, Na, P, Si, and Zn. By explaining the model predictions, we demonstrate how (dis)similarities towards known structures can be used as features for the synthesis space. Finally, we show how these methods can be used to predict inorganic synthesis conditions for unrealized frameworks in hypothetical databases and interpret the outcomes by extracting local structural patterns from zeolites. In combination with template design, this work can accelerate the exploration of the space of synthesis conditions for zeolites.


page 33

page 39


Machine learning driven synthesis of few-layered WTe2

Reducing the lateral scale of two-dimensional (2D) materials to one-dime...

Machine learning-guided synthesis of advanced inorganic materials

Synthesis of advanced inorganic materials with minimum number of trials ...

Inorganic synthesis recommendation by machine learning materials similarity from scientific literature

Synthesis prediction is a key accelerator for the rapid design of advanc...

Predictive Synthesis of Quantum Materials by Probabilistic Reinforcement Learning

Predictive materials synthesis is the primary bottleneck in realizing ne...

Too Big to Fail? Active Few-Shot Learning Guided Logic Synthesis

Generating sub-optimal synthesis transformation sequences ("synthesis re...

Rapid Bayesian optimisation for synthesis of short polymer fiber materials

The discovery of processes for the synthesis of new materials involves m...

Correlation between the Hurst exponent and the maximal Lyapunov exponent: examining some low-dimensional conservative maps

The Chirikov standard map and the 2D Froeschlé map are investigated. A f...

Please sign up or login with your details

Forgot password? Click here to reset