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AI Informed Toxicity Screening of Amine Chemistries used in the Synthesis of Hybrid Organic-Inorganic Perovskites
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  • An Su,
  • Haotian Xue,
  • Yuanbin She,
  • Krishna Rajan
An Su
Zhejiang University of Technology

Corresponding Author:[email protected]

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Haotian Xue
Zhejiang University of Technology
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Yuanbin She
Zhejiang University of Technology
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Krishna Rajan
University at Buffalo
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This paper 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 (UMAP), 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.
01 Aug 2021Submitted to AIChE Journal
10 Aug 2021Submission Checks Completed
10 Aug 2021Assigned to Editor
17 Aug 2021Reviewer(s) Assigned
09 Feb 2022Editorial Decision: Revise Minor
11 Mar 20221st Revision Received
12 Mar 2022Submission Checks Completed
12 Mar 2022Assigned to Editor
16 Mar 2022Editorial Decision: Accept
04 Apr 2022Published in AIChE Journal. 10.1002/aic.17699