Inorganic synthesis-structure maps in zeolites with machine learning and
  crystallographic distances
Daniel Schwalbe-Koda Daniel E. Widdowson Tuan Anh Pham Vitaliy A. Kurlin
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.