Conclusions
In this study, we have provided a new data-driven / AI framework for
environmentally conscious selection of amine chemistries used in the
synthesis of hybrid organic-inorganic perovskites. The selection
strategy is based on exploring high dimensional data capturing
structure-function- toxicity driven by molecular-scale information. To
the best of our knowledge, this is the first such study to critically
explore AI methods to rank toxicity impact from the perspective of
molecular descriptors; and to harness this information to identify safer
alternatives that also have been shown to be preserving the functional
performance of such perovskites for photovoltaic applications. By
coupling new probabilistic-based molecular descriptors with advanced
data dimensionality such as UMAP, we have also established a database
resource to explore other families of yet unexplored amine chemistries
that may be used for hybrid perovskite structures. The need for
searching and identifying alternative and safer chemistries for
establishing a “benign-by-design’ has long been recognized, our work
provides an example of how AI coupled to foundational materials
chemistry principles can actually facilitate an a priori approach to
select chemicals for materials synthesis that meet the
structure-function and sustainability metrics.