Artificial intelligence informed toxicity screening of amine chemistries used in the synthesis of hybrid organic–inorganic perovskites

This article describes a machine learning guided framework for screening the potential toxicity impact of amine chemistries used in the synthesis of hybrid organic–inorganic perovskites. Using a combination of a probabilistic molecular fingerprint technique that encodes bond connectivity (MinHash) coupled to non-linear data dimensionality reduction methods (Uniform Manifold Approximation and Projection), we develop an “Amine Atlas.” We show how the Amine Atlas can be used to rapidly screen the relative toxicity levels of amine molecules used in the synthesis of 2D and 3D perovskites and help identify safer alternatives. Our work also serves as a framework for rapidly identifying molecular similarity guided, structure–function relationships for safer materials chemistries that also incorporate sustainability/toxicity concerns.