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\begin{document}

\title{AI Informed Toxicity Screening of Amine Chemistries used in the
Synthesis of Hybrid Organic-Inorganic Perovskites}



\author[1]{An Su}%
\author[1]{Haotian Xue}%
\author[1]{Yuanbin She}%
\author[2]{Krishna Rajan}%
\affil[1]{Zhejiang University of Technology}%
\affil[2]{University at Buffalo}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
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.%
\end{abstract}%



\sloppy


\textbf{AI Informed Toxicity Screening of Amine Chemistries used in the
Synthesis of Hybrid Organic-Inorganic Perovskites}

An Su\textsuperscript{1,2}, Haotian Xue\textsuperscript{3}, Yuanbin
She\textsuperscript{1}, and Krishna Rajan\textsuperscript{2}*

1. College of Chemical Engineering, Zhejiang University of Technology,
Hangzhou, Zhejiang, 310014, China

2. Department of Materials Design and Innovation, University at Buffalo,
Buffalo, New York 14260-1660, United States

3. Collaborative Innovation Center of Yangtze River Delta Region Green
Pharmaceuticals, Zhejiang University of Technology, Hangzhou, Zhejiang,
310014, China

*Corresponding Author:

Krishna Rajan

orcid.org/0000-0001-9303-2797

Email: \emph{krajan3@buffalo.edu}

\section*{Abstract}

{\label{abstract}}

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.

\textbf{Topical Heading}

AIChE Journal Special Issue on ``Artificial Intelligence in Chemical
Engineering''.

\textbf{Keywords}

artificial intelligence, machine learning, hybrid organic-inorganic
perovskite, amine chemistry, toxicity screening

\section*{Introduction}

{\label{introduction}}

In recent years, organic-inorganic perovskites have received a huge
amount of attention due to their promise for photovoltaic
application\textsuperscript{1-3}. Generally, perovskite refers to the
ABX3 three-dimensional structural frameworks with ``A'' as an organic
cation (usually the cation of amines), ``B'' as a metal, and ``X'' as a
halogen.\textsuperscript{1,2}. The chemical diversity of amines used in
perovskites for PV applications is large as they appear in both 3D and
2D perovskite structures.\textsuperscript{4,5} For instance, in a recent
review study by Saparov and Mitzi, more than 60 distinct perovskite
amine structures have been mentioned for the design of versatile
perovskite materials.\textsuperscript{6}

With the rapid development of new classes of perovskite chemistry with
promising physical properties, the concerns in identifying amine
chemistries that have a minimal environmental/ health footprint become
more challenging.\textsuperscript{7} In looking at the full life cycle
of perovskites used in PV applications, we now need to consider the
toxicity of the organic molecules, along with metal elements such as Pb,
at all stages of the materials synthesis and
degradation.\textsuperscript{8-17} For example, previous studies on the
toxicity of amines have concluded that many aromatic amines (e.g.
aniline, diphenylamine) are potential carcinogens, while the aliphatic
amines are less hazardous.\textsuperscript{18-21} However, in aquatic
environments, there are potential formations of toxic compounds
including nitrosamines and nitramines from the reaction between amines
and nitrite oxidants.\textsuperscript{19,21} As the perovskite materials
become more and more popular, it is critical to perform a systematic
study on the toxicity of both the existing amines that have been part of
the 3-D and lower-dimensional perovskites and the potential perovskite
amines that have similar structures to the existing ones. In this study,
we present a machine learning aided molecular structure-toxicity
analysis to screen the potential toxicity of amines used for the
synthesis of hybrid organic-inorganic perovskites.

Our training and test database of perovskite amines is based on open
source literature along with a structural similarity search on PubChem
(https://pubchem.ncbi.nlm.nih.gov), a well-acknowledged database for
chemical structure and functionality\textsuperscript{22}. For toxicity
data of perovskite amines, instead of fetching data from different
literature without a common standard, we performed searches on PubChem
Bioactivity Assays database\textsuperscript{23} which is based on
similar data standards that are more suitable to study at a systematic
level. This study aims to establish a structure-toxicity relationship of
perovskite amines, help identify safer alternatives for use in
perovskite structures.

\section*{Methods}

{\label{methods}}

As shown in Figure 1, the research consists of five main steps: the
curation of perovskite amines database, the two-level classifications of
perovskite amines, the chemical informatics and machine learning
computations, the search for toxicity data, and the visualization of
Amine Atlas.

\textbf{Curation of perovskite amines database.} The amines
corresponding to the perovskite ammonium cations mentioned in recently
published reviews\textsuperscript{6,24} and
database\textsuperscript{25}are sorted into the ``existing perovskite
amines'' list (e.g., ethylamine corresponds to ethyl ammonium). This
list is the basis of the perovskite amines database. Next, the database
is further expanded by including ``potential perovskite amines'', which
have similar structures to existing amines. The PubChem similar
structure search is performed on each existing perovskite amine, and the
similarity threshold is 95\%. By removing ions, non-amines, and existing
structures in the database, the amines in the search results are further
screened. In addition, these amines must have been tested by at least
one activity assay in PubChem BioAssay database\textsuperscript{23,26}
in order to be included as potential perovskite amines. The above search
and filtering steps are completed by our programming tool based on the
open-source Python packages PubChemPy
(\emph{https://pubchempy.readthedocs.io/en/latest/}) and RDKit
(\emph{https://www.rdkit.org/docs/api-docs.html}). In the final
database, each amine has its PubChem Compound ID (CID), name, SMILES,
and a list of its corresponding PubChem Bioassay ID (AID). The CID and
name of their corresponding ammonium cations are also included.

\textbf{Two-level classification.} The amines are first classified
according to their aromaticity and the position of their amine group
(e.g. on aromatic ring, directly attached to the aromatic ring, or on
the alkyl substituent of the aromatic ring). Further subclass
classifications are established based on more detailed structures such
as functional groups and linearity of alkyl chains). The identification
of functional groups and chemical fragments is achieved through our
RDKit-based programming tool (provided in supplementary material). It is
worth noting that the purpose of classification here is not to establish
a new standard of amine classification but to distinguish the amines in
our database as much as possible.

\textbf{Chemical informatics and machine learning computations.} The
MHFP6 fingerprint\textsuperscript{27} is calculated for each perovskite
amine molecule using the open-source Python package MHFP
(\emph{https://github.com/reymond-group/mhfp}). The dimensionality of
the fingerprint is then reduced by Uniform Manifold Approximation and
Projection (UMAP)\textsuperscript{28} with the open-source Python
package UMAP
(\emph{https://umap-learn.readthedocs.io/en/latest/index.html}). The
parameters of these two tools, including the number of permutations of
MHFP and the number of neighborhoods and the minimum distance of UMAP,
are optimized for the clustering of different amine classes and
subclasses. The data processing steps during the computations are
achieved with the open-source Python packages Pandas
(\emph{https://pandas.pydata.org/docs/}) and
Scikit-learn\textsuperscript{29}. The code for the computations is
provided in the supplementary material.

\textbf{Search for toxicity data.} The detailed information of all the
bioassays with AID recorded in our perovskite amines database is
retrieved from PubChem Bioassay Database\textsuperscript{23,26} using
our PubChemPy-based programming tool. Only the bioassays with more than
one perovskite amine showing ``active'' are kept. In addition, the
bioassays showing bioactivity other than toxicity are eliminated.
Finally, a table of PubChem Bioassays with their AID, number of
perovskite amines tested as active, and assay name is obtained.

\textbf{Visualization of Amine Atlas.} The Amine Atlas is visualized
using Plotly, a Python open-source graphing library
(\emph{https://plotly.com/python/}). The Amine Atlas can be viewed with
or without amine toxicity data. In Amine Atlas, each data point
represents an amine---UMAP calculation results are used as the
two-dimensional coordinates of the data point, and the classification
results or the hit ratio of the compound is displayed in the color of
the data point. The detailed information of the corresponding compounds
of the data point is displayed in the hover data tab, such as CID,
SMILES, type (existing or potential), and classifications. All Amine
Atlas shown in the following sections have corresponding interactive
versions in the supplementary material.

\section*{Results and Discussion}

{\label{results-and-discussion}}

The first part of our curated perovskite amine database consists of 184
amines that correspond to the ammonium cations in literature, named
``existing perovskite amines''. The structural similarity search on
PubChem and further screening process give an additional 264 amines that
are considered ``potential perovskite amines'' ---the amines that have
similar structures to existing ones. Finally, the curated perovskite
amine database contains 448 amine structures. The full table of the
database is provided in the supplementary material. The main reason for
expanding the database is to make full use of data on amines that have
been tested for bioactivity or toxicity, regardless of whether they have
been studied as perovskite amines. As more amines are included in the
analysis, it may be easier to find toxicity trends and their
relationship to the amine structure.

The introduction of artificial intelligence to the creation of Amine
Atlas and toxicity screening of amine chemistries involves the
calculation of MinHash fingerprint, up to six bonds (MHFP6) and Uniform
Manifold Approximation and Projection (UMAP). MHFP6 is an improved
version of the extended connectivity fingerprint
(ECFP)\textsuperscript{27} that lowers the dimensionality needed to
describe the detailed molecular substructures as well as increases the
performance of the nearest neighbor search.\textsuperscript{30} The
MHFP6 fingerprint has been used in recently published chemistry
databases\textsuperscript{31,32} and data visualization
tool\textsuperscript{33} with big data settings. MinHash is a locality
sensitive hashing (LSH) scheme that applies a family of hashing
functions to the substrings in molecular shingling and stores the
minimum hash generated from each hashing function in a set. These sets,
containing the minimum hash values, have the interesting property that
they can be indexed by an LSH algorithm for approximate nearest neighbor
search (ANN), removing the curse of
dimensionality.\textsuperscript{30}MinHash allows for the indexing of
chemical structures in extremely sparse Jaccard (Tanimoto) space, a
metric more appropriate for fingerprint-based similarity calculations.
\textsuperscript{30} On the other hand, UMAP is a recently developed
non-linear dimensionality reduction algorithm\textsuperscript{28} that
has been used to analyze various types of scientific data, mainly in the
field of biological sciences including genome
aggregation\textsuperscript{34}, single-cell mass flow
cytometry\textsuperscript{35}, and single-cell RNA sequencing
(scRNA-seq)\textsuperscript{35-37}. UMAP is a manifold learning method
that preserves local and global structure of the high-dimensional data
points by minimizing data/information loss. It explores the network
connectivity using K-nearest neighbor distance (KNN) over a
high-dimensional hyperplane and then estimates a low-dimensional
coordinate system that replicates the same graph structure, preserving
the edge connectivity of the high-dimensional by keeping graphical
representation intact in the low-dimensional space. Compared with the
more frequently used \emph{t} -distributed stochastic neighborhood
embedding (t-SNE) algorithm which has limited capability to represent
the global structure of the data, it is found that UMAP retains the
local and global structure of the data by simultaneously capturing the
small differences and the continuity between the data subsets.

The higher level of classification gets amines categorized into
aliphatic amines (cyclic and noncyclic), heterocyclic aromatic amines,
and other aromatic amines including phenylalkyl amines and anilines.
Combining this classification information with the results of the UMAP
on the MHFP6 fingerprint of perovskite amines, the clustering of these
amine classes can be observed on Amine Atlas. The optimized clustering
is reached when MHFP permutation number, UMAP number of neighbors, UMAP
minimum distance are set to 2048, 50, and 0.25, respectively. Using this
combination of parameters, the main classes are well-separated from each
other on the Amine-Atlas (Figure 2), and the same parameters are used
for all the Amine Atlas below.

For each amine class, the Amine-Atlas can display further
classifications as subclasses. The subclasses of heterocyclic aromatic
amines are shown in Figure 3. This class of amines is clearly divided
into common nitrogen-containing aromatics, including pyrrole, imidazole,
pyridine, and thiazole, and sulfur-containing thiophene. No overlap is
observed between the clusters, which may be due to the effectiveness of
MHFP6 fingerprint in capturing the characteristics of common aromatic
compounds.

Similarly, for the class of phenylalkyl amines, the subclasses are
well-separated in Amine-Atlas (Figure 4). This figure shows the power of
UMAP in capturing both the local and global structure of the data. Here,
the UMAP captures subtle differences between subclasses (such as those
with the same carbon number) by dividing them into different clusters
(e.g. 1-phenylethylamines
(C\textsubscript{6}H\textsubscript{5}-C(C)NH\textsubscript{3}) and
phenylethylamines
(C\textsubscript{6}H\textsubscript{5}-CCNH\textsubscript{3})). At the
same time, the UMAP shows the continuity of close subclasses by placing
them in adjacent positions, such as the benzylamines
(C\textsubscript{6}H\textsubscript{5}-CNH\textsubscript{3}) and
phenylethylamines
(C\textsubscript{6}H\textsubscript{5}-CCNH\textsubscript{3}) whose alkyl
substituents differ in chain length by 1.

Due to the complex structure of branched alkyl chains, the noncyclic
aliphatic amines have some clusters with less organization (Figure 5).
However, the trend still exists in the amines with linear alkyl chains,
such as the linear diamines (purple) and linear monoamines (orange)
subclasses, where the length of the alkyl chain decreases along the
UMAP-1 axis. In addition, amines that have functional groups in addition
to amine groups (dark green) are distant from unsubstituted amines
(purple and orange).

One important purpose of this study is to screen the relative hazard of
amines being used in 2D and 3D perovskite synthesis -- those most
hazardous and those not so. We retrieve the toxicity data of perovskite
amines from PubChem Bioassay Database\textsuperscript{23,26}, an
open-source repository holding a collection of bioactivity and toxicity
data of small molecules---these molecules are cross-linked to the data
of their chemical structures stored in PubChem Compounds
Database\textsuperscript{22}. After a search using our programming
tools, we summarized a list of PubChem Bioassays that focus on the
toxicity of chemicals and in the meantime include perovskite amines as
test substances, and the complete list of assays is provided in the
supplementary material. Examples of the toxicity effects and
corresponding AID are shown in Table 1.

\textbf{Table 1.} Examples of selected PubChem Bioassays and the
toxicity effect they study\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
AID & Toxicity effect\tabularnewline
\midrule
\endhead
743122 & Activator of the aryl hydrocarbon receptor (AhR)\tabularnewline
1224892 & Agonists of the constitutive androstane receptor
(CAR)\tabularnewline
1347033 & Agonists of Human pregnane X receptor (PXR)\tabularnewline
720637 & Disruptors of the mitochondrial membrane potential
(MMP)\tabularnewline
743219 & Agonists of the antioxidant response element
(ARE)\tabularnewline
1159553 & Agonists of the retinoic acid receptor (RAR)\tabularnewline
743079 & Agonists of the estrogen receptor alpha
(ER-alpha)\tabularnewline
743078 & Antagonists of the estrogen receptor alpha
(ER-alpha)\tabularnewline
1259247 & Antagonists of the androgen receptor (AR)\tabularnewline
1259396 & Antagonists of the estrogen receptor beta
(ER-beta)\tabularnewline
\bottomrule
\end{longtable}

According to the test results of PubChem Bioassays, we use the hit ratio
(the ratio of active substances to the total number of screening
targets) to indicate the overall toxicity of our perovskite amines. By
plotting hit ratio data on Amine Atlas (Figure 6), rapid screening of
structure-toxicity relationships can be established. By comparing Figure
6 with Figure 2 and Figure 3, it is found that most of the toxic
perovskite amines are in the anilines cluster, while a few pyrroles and
pyridines in the heterocyclic aromatics also have a hit ratio
\textgreater{} 0.3. Meanwhile, the aliphatic amines are less toxic, and
most of the toxic aliphatic amines are cyclic. It should be noted that
compared with other amines, phenylalkyl amines have very little toxicity
data, so care should also be taken when using these amines.

In addition to providing the information extracted from the Amine Atlas,
we also provide statistical analysis on the toxicity data from PubChem
Bioassays without the involvement of AI. We recommend that researchers
and manufacturers use the 10 safest existing perovskite amines (Table 2)
and potential perovskite amines (Table 3) as they have the lowest hit
ratio, and, if possible, avoid using amines with high hit ratio (Table
4). It is worth noting that our recommendation of the safer potential
perovskites is entirely based on our research from the perspective of
toxicity---people should determine the chemical or physical properties
of these amines according to their scientific or industrial needs.

\textbf{Table 2.} Ten safest existing perovskite amines ranked by hit
ratio (the fraction of actives out of the total number of screened
targets)\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
CID & Name & SMILES & Class & Subclass & Hit Ratio\tabularnewline
\midrule
\endhead
674 & Dimethylamine & CNC & Aliphatic (noncyclic) & Short amine (heavy
atoms \textless{} 5) & 0/44\tabularnewline
1146 & Trimethylamine & CN(C)C & Aliphatic (noncyclic) & Short amine
(heavy atoms \textless{} 5) & 0/44\tabularnewline
1390 & 1-Methylimidazole & Cn1ccnc1 & Heterocyclic aromatic & Imidazole
& 0/28\tabularnewline
24874 & 2-Butanamine & CCC(C)N & Aliphatic (noncyclic) & Branched
monoamine & 1/45\tabularnewline
10009 & 3-Aminopyridine & Nc1cccnc1 & Heterocyclic aromatic & Pyridine &
1/44\tabularnewline
31018 & 3-(Aminomethyl)pyridine & NCc1cccnc1 & Heterocyclic aromatic &
Pyridine & 1/44\tabularnewline
774 & Histamine & NCCc1cnc{[}nH{]}1 & Heterocyclic aromatic & Pyrrole &
1/44\tabularnewline
7515 & N-Methylaniline & CNc1ccccc1 & Aniline & Aniline &
2/48\tabularnewline
9257 & 1,2,4-Triazole & c1nc{[}nH{]}n1 & Heterocyclic aromatic & Pyrrole
& 2/44\tabularnewline
4091 & Metformin & CN(C)C(=N)N=C(N)N & Aliphatic (noncyclic) &
Multi-amine & 2/44\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3.} Ten safest potential perovskite amines ranked by hit
ratio\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
CID & Name & SMILES & Class & Subclass & Hit Ratio\tabularnewline
\midrule
\endhead
13032 & N-Isopropylaniline & CC(C)Nc1ccccc1 & Aniline & Aniline &
0/47\tabularnewline
119716 & Isononanamine & CC(C)CCCCCCN & Aliphatic (noncyclic) & Branched
monoamine & 0/44\tabularnewline
7096 & 1-Phenylpiperazine & c1ccc(N2CCNCC2)cc1 & Aniline & Aniline &
0/32\tabularnewline
60993 & N,N-Diethylethylenediamine & CCN(CC)CCN & Aliphatic (noncyclic)
& Branched diamine & 0/32\tabularnewline
1727 & 4-Aminopyridine & Nc1ccncc1 & Heterocyclic aromatic & Pyridine &
0/48\tabularnewline
7973 & 2-Bromopyridine & Brc1ccccn1 & Heterocyclic aromatic & Pyridine &
0/27\tabularnewline
14310 & N-Butylaniline & CCCCNc1ccccc1 & Aniline & Aniline &
0/44\tabularnewline
1049 & Pyridine & c1ccncc1 & Heterocyclic aromatic & Pyridine &
1/48\tabularnewline
6115 & Aniline & Nc1ccccc1 & Aniline & Aniline & 1/48\tabularnewline
13195 & 4-Methylimidazole & Cc1cnc{[}nH{]}1 & Heterocyclic aromatic &
Pyrrole & 1/47\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 4.} Ten most hazardous existing perovskite amines ranked
by hit ratio\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
CID\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Name\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
SMILES\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Class\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Subclass\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Hit Ratio\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7472\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
N,N-Dimethyl-p-phenylenediamine\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
CN(C)c1ccc(N)cc1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
35/47\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7814\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
p-Phenylenediamine\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Nc1ccc(N)cc1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
29/44\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7111\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Benzidine\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Nc1ccc(-c2ccc(N)cc2)cc1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
28/47\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7807\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4-Bromoaniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Nc1ccc(Br)cc1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
24/48\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7102\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4-Aminobiphenyl\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Nc1ccc(-c2ccccc2)cc1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
23/47\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
16720\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1,5-Naphthalenediamine\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Nc1cccc2c(N)cccc12\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
21/48\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4837\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Piperazine\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
C1CNCCN1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aliphatic (cyclic)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Piperazine\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
19/44\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
13583\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Dodecylamine\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
CCCCCCCCCCCCN\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Aliphatic (noncyclic)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Linear monoamine\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
18/48\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
70248\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
1,8-Diamino-3,6-dioxaoctane\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
NCCOCCOCCN\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Aliphatic (noncyclic)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Functionalized amine\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
12/44\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7812\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4-Chloroaniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Nc1ccc(Cl)cc1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Aniline\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
13/49\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\section*{Conclusions}

{\label{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.

\section*{Acknowledgments}

{\label{acknowledgments}}

The authors acknowledge the support from NSF Award\# 1640867 -- DIBBs:
EI: Data Laboratory for Materials Engineering and the Collaboratory for
a Regenerative Economy (CoRE center) in the Dept of Materials Design and
Innovation -- University at Buffalo\textbf{.}

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