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ArticleStructure−Property Correlation Study for Organic Photovoltaic
Polymer Materials Using Data Science Approach
Published as part of The Journal of Physical Chemistry virtual special issue “Machine Learning in Physical
Chemistry”.
Yue Huang,*,# Jingtian Zhang,# Edwin S. Jiang, Yutaka Oya, Akinori Saeki, Gota Kikugawa,Tomonaga Okabe, and Fumio S. Ohuchi*
Cite This: J. Phys. Chem. C 2020, 124, 12871−12882

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ABSTRACT: A study workﬂow that utilizes several data science methods to apply on polymer materials databases is introduced toreveal correlations among their properties, structural information, and molecular descriptors. The data science methods used in thispipeline include the unsupervised machine learning (ML) method of self-organizing mapping (SOM) and the polymer molecular
descriptor generator, both of which have been tailored to ﬁt the polymer materials study. To demonstrate how this pipeline can beapplied in this context, we used it on an organic photovoltaic (OPV) donor polymer database to investigate which properties or
structural factors positively correlate with the power conversion eﬃciency (PCE) of OPV materials. This led us to discover thatamong the studied 8 properties and 11 molecular descriptors, only the photon energy loss (Eloss) and the number of ﬂuorine atoms(nF) show strong positive correlations with PCE values, which is consistent with other veriﬁed studies. We also discovered that
research trends can also be statistically visualized using our method. In our case study, we found that most of the studied OPV donor
materials in the database have branched side chains and typically 7−12 non-hydrogen atoms, and high PCE materials usually have
6−9 aromatics rings as well. These results proved that the data science pipeline proposed in this study provides a fast and eﬀective
way to obtain research insights for polymer materials.

1. INTRODUCTION

breakthroughs in scientiﬁc research, including in the ﬁeld ofmaterials research.
In particular, as it comes to polymer design research,molecular ﬁngerprints and descriptors, such as molecular

We are at a time when data science is becoming applicable toalmost any research ﬁelds. There are two major reasons for this:one, data science and computational developments haveprogressed greatly in the past years to the point where many
tools and algorithms developed for other research ﬁelds havebecome very powerful, adaptable, and more easily accessible;
two, the development of the research instrumentation makes
science research more eﬀective and eﬃcient in generating data,
and the size of the data in almost all research ﬁelds is increasing
rapidly. New data science approaches now lead to many
© 2020 American Chemical Society

Received: January 18, 2020
Revised: April 20, 2020
Published: April 24, 2020

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Articleand include mainly three categories of information about apolymer: (a) chemical, such as diﬀerent chemical elements or
diﬀerent functional groups existing inside of the polymer, (b)geometrical, such as linear or ring structured, and (c) diﬀerent
types of branching for the side chains. The number of molecular
descriptors of polymers can be in the thousands,14 and theproperties or performances of a polymer can change with any ofthem. Moreover, one molecular descriptor may impact theproperties diﬀerently when the other molecular descriptors
change. If all these molecular descriptors are included in the
training set of SOM, this extremely high dimensionality training
set will cause all materials to appear sparse and dissimilar in thehigh dimensional space, making “clustering and organization”
almost impossible. This problem is usually described as the“curse of dimensionality”.15
To avoid this problem, we adopted the “information
projection” function of the SOM that was developed anddescribed in our earlier study.12 In that study, this function
helped us by projecting categorical data onto the SOM cluster
map to ﬁnd the correlation between measured properties andcategories of materials. In this study, we project the numerical
data, including properties and molecular descriptors, onto thecluster map. This helps us eﬀectively and eﬃciently identify
which geometrical or chemical structure will likely give us themost desired property. This information projection process is
illustrated in Figure 1.

electronegativity, molecular coordinates, number of atoms, etc.,
can be used as inputs to machine learning (ML) models toscreen for well-matched polymer materials or generating
polymer structures that can lead to improved performance.
The ML methods used in such research can be classiﬁed into two
categories, supervised ML or unsupervised ML.
In supervised ML, several intriguing researches1−4 have beenaccomplished using the random forest (RF) classiﬁer to generate
the classiﬁcation of polymers performance based on theirmolecular information. This pipeline is very successful in
expediting the screening process for polymer design, synthesis,
and characterization, but usually requires signiﬁcant humaneﬀorts in conducting data training.
In unsupervised ML, recent progress has focused on utilizing
dimension reduction methods such as principle component
analysis (PCA),5 t-distributed stochastic neighbor embedding(t-SNE),6 and self-organizing map (SOM)7 to analyze
correlation across properties. With materials’ properties having
diﬀerent levels of correlation and fundamental connection, theirdatasets naturally have a high dimensionality. Among thedimension reduction methods mentioned, t-SNE is popular for
its nonlinear dimension reduction and visual presentation ofhigh dimensional data in a low dimensional space. It was used inprotein study to classify proteins based on simulation calculated
properties,9 and in another study, previously uncharted band
structure space for thousands of materials are clustered based on
the DFT calculated energy dispersion data.10 Although limited
publications can be found focusing on demonstrating the resultsof PCA analysis, this method is widely used as a reference in
conﬁrming the correlation between structure and particularproperties mathematically.5,8−10 However, most research foundthat PCA can cause unavoidable loss of physical meaning,whereas t-SNE and SOM are more favorable to study highdimensional data for human-intuitive visualizations. In comparison with t-SNE, although relatively new to the community,
SOM is more eﬃcient in unraveling nonlinear and unordereddata in real cases.11 Therefore, it is more suitable for materialscience research to ﬁnd correlations among diﬀerent properties,
or correlations between properties and structure of thematerials. A detailed explanation of how to use SOM inmaterials science can be found in our earlier publication12 inwhich we were able to cluster and discover correlations ofproperties among 20 properties simultaneously. None of theseresearches can be conducted manually without eﬃcient data
science methods.
Even though data science methods can eﬃciently classify
hundreds, or even thousands materials based on their properties,
not so many functions and methods have been developed to
discover correlation patterns between particular properties andstructure of the materials, particularly for the polymer materials,
and the application of SOM on polymer study has been limited
so far. In this study we will demonstrate how correlation among
structure and properties of polymer can be discovered byintroducing a new data science workﬂow which can be used to
improve the eﬃciency of polymer design.
1.1. SOM and the Challenge of Its Application in
Polymer Informatics. One of the diﬃculties and challenges inapplying dimension reduction to polymer systems stems fromthe fact that there are many more distinctive factors diﬀerentiating one polymer structure from another than in inorganic
materials.13 In polymer informatics, these distinctive factors of
the structures, or the mathematical and logical representationsof the molecular conﬁguration are called “molecular descriptors”

Figure 1. Molecular descriptors used for this project are projected on
the cluster map by color coding the dots, which each correspond to aunique polymer ID. This enables visualization of their distributions onthat map. Diﬀerent colors correspond to various ranges of normalized
values for the projected property.

Molecular descriptors or geometrical and chemical information on monomers can be extracted by molecular descriptor
generator from SMILES (Simpliﬁed Molecular-Input LineEntry System), a widely used cheminformatics tool to describe
molecular structure in a one-line short ASCII string.16 It was
initiated in the 1980s17,18 and then commonly used in chemical
informatics studies.1,19,20 However, for experimental researchersor researchers who do not base their research on computational
tools, the usage of SMILES is quite limited. Without a proper
tool to help us organize and use information from SMILES, itcannot be used directly. Molecular descriptors can be expressedfrom SMILES by using the Mordred Descriptor Generator(MDG).21 MDG is an open-source python library based on thecheminformatics toolkit Rdkit.22 When compared to otheravailable open-source software, such as BlueDesc,23 PyPDI,24
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ArticleFigure 2. Data processing pipeline, including input, tools, and output. Information is projected on the cluster map to visualize the property−structure
relationship after normalization. Heatmaps are used to visualize property−property correlations and are produced directly from SOM training.

Figure 3. Samples of p-type donors’ monomer structures in the data set. A total of 1203 polymers are included, which are the studied donor polymer ina fullerene acceptor OPV system.

Rcpi,25 Dragon,26 etc., it has advantages of being able to generate
up to 1825 molecular descriptors and to support parallel
computation, web interface, and command-line interface. The
workﬂow adopted in this project is shown in Figure 2, where we
combine both SOM and molecular descriptor generators to
study OPV materials.
1.2. Database and Experimental Variables. To demonstrate how this study pipeline was utilized, we applied our
methods to a data set of donor materials in the organic
photovoltaic (OPV) devices with fullerene acceptor. Databaseused in the present study was built by Nagasawa et al.,1 and theobjective was to understand how the properties were correlatedwith each other, or to the molecular descriptors.
In the OPV devices, measurements commonly taken toaccurately characterize functionality of the devices and materials
include short-circuit current density (Jsc), open-circuit voltage(Voc), ﬁll factor (FF), various means of molecular weights (e.g.,weight-averaged one, Mw, and number-averaged one, Mn),
spectral absorption, internal and external quantum eﬃciencies(IQE and EQE),1 and others. Performance of the devices isgenerally evaluated by the power conversion eﬃciency (PCE),27
of which values were used as the main indicator of theperformance of the OPV devices.
The data set used in this study contains a total of 1203
diﬀerent types of organic photovoltaic polymers, of which
experimental data were manually collected from more than 500
papers. Several examples of the p-type polymers in the data set
are illustrated in Figure 3. This data set contains SMILES and 11
properties for each of these polymers, including the Voc, Jsc, FF,
molecular weights (Mw, Mn, and the weight of monomer unit),
polydispersity index (PDI), principal energy levels (highest

occupied molecular orbital, HOMO, and lowest unoccupied
molecular orbital, LUMO), optical bandgap (Eg), photon energy
loss (Eloss), and PCE.
In this paper, we investigated the correlation between 11
molecular descriptors and the PCE performance of the material
in our data set. The selected 11 molecular descriptors are theexistence of 4 elements (nitrogen (N), oxygen (O), sulfur (S),ﬂuorine (F)), the number of side chains (nChain), the numberof all aromatic rings (naRing), the number of ﬁve-memberedaromatic rings (n5aRing), including pyrrole, pyridine, furan andthiophene, the number of six-membered aromatic rings(n6aRing), including cyclohexane and benzene, the ratio
between nChain and naRing (nChain/naRing), and whether
the material contains branched side chains and/or linear side
chains. These are geometrical and chemical features that are
possibly associated with PCE values of the device in reported
studies.11,28,29
In this project we do not discuss 3D molecular descriptors
from SMILES since they are mostly related to the molecularconformation and electronegativity. For example, 3D descriptors provided by MDG include radius and diameter of the entire
molecule, fractional charge partial negative surface area,geometric shape index, etc. These descriptors are diﬃcult toanalyze since many factors have convoluted impacts on the
outputs. We are focusing on investigating 2D descriptors in thiswork.
We also studied other material properties that could becorrelated to the PCE values, including Eg and Eloss. Here, Eloss, as
expressed in eq 1,30 is preliminarily determined by variations inenergetic oﬀset between the Eg and Voc with values ranging
between 0.6 and 1.0 eV. Several theoretical and empirical studies
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have demonstrated the importance of reducing Eloss to reachmaximum possible Voc for devices.1
E loss = Eg − qVoc

Articleresults of the PCE cluster map are shown in Figure 4, in which
the high PCE value polymers are well clustered into several(1)

where q is the elementary charge.
In this paper, we constructed a research workﬂow that
includes several data science methods to demonstrate how thedata science methods can be utilized in searching for correlation
between the structural or chemical descriptors and the polymer
properties.
In the following sections, we describe data feature selection
for ML, SOM training, and visualization of the data set, so thatclustering of the materials reﬂects the PCE performance of thematerials. We then describe the method used to extract
molecular descriptors from SMILES and eventually discuss
how the extracted molecular descriptors and other properties areprojected to the 2D cluster maps in order to study thecorrelations between these information and material performances. Here, we point out that, even though very speciﬁc systems
are studied in this case study, the method we describe in thisstudy can be used in other data sets, such as nonfullerene
acceptor systems that are attracting more research interests
lately.31−34

Figure 4. Projected PCE value map. The PCE values from the original
data set are divided into 5 regions in diﬀerent colors by min−max
normalization. PCE boundary regions (red and blue line) are drawn out
on the basis of the data distribution. The red line indicates the top 40%of the PCE values, and the blue line indicates the lowest 20% of the PCE
value.

2. METHODS
2.1. Feature Selection for SOM. In ML, feature selection isa critical step where features, or properties, are chosen to be used
as input. In our study, the design matrix for model training isformed by 5 dominant features, Jsc, Voc, FF, Mw, and HOMO.
The ﬁrst three, Jsc, Voc, and FF, are direct contributors to the
PCE values, whereas Mw and HOMO are chosen for theirpromising correlations with PCE. It has been known that Mw
tends to give enhanced PCE for the same molecular structure
and backbone and also creates a more ﬁbrous structure of thepolymer domain in a BHJ ﬁlm.1,35 In addition, higher Mw with anarrow distribution leads to a higher degree of crystallinity.36−38
HOMO is chosen because deepening of HOMO results in an
increased Voc. With this combination, relative distances between
the data points were preserved by ensuring the best preservationof the topology, therefore yielding better results.7,11 More
importantly, the properties we have chosen in the feature
selection gave a good clustering of the materials in the PCE
values. This will be discussed in the next section.
2.2. Training and Visualization by SOM. Both the datavisualization and training come from an updated version of apython package called tfprop_sompy by Kikugawa andNishimura39 based on the Python package SOMPY.40 The
data processing and visualization works are done using multiple
Python packages, including Pandas, Numpy, Scikit-learn, andMatplotlib.
Output of the SOM model consists of a set of 2D arrays
including cluster map, heat maps, and U-matrix map. Amongthese, we focus on the visualization of cluster map and heatmaps,
as described in the project workﬂow in Figure 2. The U-matrix
maps were used only to benchmark the results of the clustering
map to prove that our clustering map does represent thecloseness of the studied properties of the polymer materials. The
cluster map provides where the materials are located on the 2D
array map, whereas the heatmaps are used to visualize thepattern of the properties’ distribution in the design matrix. The
PCE values are also projected from the database to conﬁrm that
our clustering map does put materials with high PCE together
and, therefore, reﬂects the performance of the materials. The

regions on the right side of the cluster map, and the low PCE
value polymers in general are accumulated on the left side. In ourpolymer−fullerene OPV database, the PCE value ranges from
0.01% to 10.50%. To highlight the statistical signiﬁcance of our
method, we outline the polymers with a top 40% PCE value fromour data set (PCE value higher than 6.3%) and focus our analysis
on the correlation between the other factors with thesepolymers. For our study, we call these polymers high-PCE
polymers.
In this study we used a map size of 80 × 80, cluster numbers of
4 to incorporates all 1203 samples, and 5 selected features andgenerated the cluster map that no node accommodates morethan one data point. With these parameters, the topographic
error and quantization error of the cluster maps is minimized to
0.0116376 and 0.128816, respectively. SOM training was madeon a local laptop with a 1.8 GHz eighth generation Intel Corequad-core processors of Intel (R) Core(TM) i7-8550U in 447.5
s.
2.3. Extraction of Information from SMILES. The python
library Mordred Descriptor Generator includes most of themolecular descriptors that are studied in this project.41 In ourcase study, a 1203 × 1825 matrix table was generated
automatically with the numerical counts of every descriptor
for each material from the 1203 instances using SMILES as the
input ﬁle. The 11 molecular descriptors that are of interest werethen selected manually for the information projection step of our
workﬂow.
Other geometrical information of interest, such as thepresence of a branched side chain or a linear side chain, wereextracted by using data processing with some parsing rules
directly from the SMILES ASCII code. All the data and thealgorithm can be found at the GitHub address: https://github.
com/DataScienceUWMSE/SOM_OPV.
2.4. Information Projection. The most important step inour method is to project information on the cluster maps toreveal correlations between structural information and perform12874

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ArticleFigure 5. Heatmap for features, including (a) Voc, (b) Jsc, (c) FF, (d) Mw, and (e) −HOMO. PCE boundaries are shown on the maps using the blackbold lines.

For every numerical data point of the property x , we compute a
normalized data point x′ and a label corresponding to it. k is thenumber of classes we want the property to be divided into, and
0.499 is a constant value to round up label values in order tomake distribution of the results more uniform. The calculated
label after rounding should always range between 0 and k. Then,a dot that represents each material is colored on the basis of thelabel corresponding to a range of the projected value thismaterial belongs to. As shown in the illustration of the projectionin Figure 1, diﬀerent colors correspond to diﬀerent ranges ofnormalized values for the projected property.
In the next section we will ﬁrst discuss the results andconclusion we can obtain from the heat maps and then discuss
the results from projecting the molecular descriptors onto thecluster map.

ance of the materials. Since the cluster map has already groupedon the basis of the properties of interest (PCE performance inour study case), we are able to visually observe the distributionof the projected information. If high values of the projected
information coincide well with high values of the properties
presented in the cluster map, then a positive correlation can beconcluded; otherwise, there is no correlation or a negative
correlation between the projected information and the PCE
value.
Through our study, we sometimes ﬁnd that projection of thenumerical data directly on the maps can be diﬃcult to interpret.
In the projection function, diﬀerent numerical values areprojected to the cluster map using diﬀerent colors. If the rangeof values of the projected data is large, too many colors are used,which require users to diﬀerentiate colors that are very similar toeach other or only have subtle gradient changes. Thus, featurescaling is required on all numerical data to make interpretationeasier. In this case we applied the min−max normalization
technique:42x′ =3. RESULTS AND DISCUSSION
3.1. Heat Maps. Figure 5 presents heat maps of theproperties of Voc, Jsc, FF, Mw, and −HOMO, from which we canvisually see how these property values are distributed on thecluster map. It is apparent that there is a trade-oﬀ eﬀect between
Jsc and Voc on their heatmaps, whereas majority of the high PCE
materials has a low-lying HOMO or medium HOMO. This
could be rationalized by the fact that lower-lying HOMO levels
contributes to larger Voc in BHJ devices.43 Jsc and FF have similar

xi − min(x)max(x) − min(x)label i = round(x′ × k + 0.499)
k ≥ labeli ≥ 0

(2)
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ArticleFigure 6. Information projected cluster map of (a) Eloss and (b) Eg. (c) Monomer structures generated from monomer SMILES of materials marked bytriangles in (a) and (b) with high PCE, low Eloss, and low Eg. (d) Monomer structures marked by rectangles in (a) and (b) with high PCE, high Eg, andhigh Eloss. High PCE region are shown on the cluster maps in red, and low PCE region, in blue lines.

absorption proﬁle and the absorption intensity can besigniﬁcantly inﬂuenced by the molecular weight.45−47
The heat maps provide visualizations displaying compressed
information on the original high-dimensional data on anorganized 2D mesh. The x axis and y axis of the 2D mesh,which are the same in the heat maps and in the cluster map, areunitless and without any physical meaning. They provide a
distribution of the materials that are studied in the ML learning.
The materials with the same ID locate at the exact same positionon all heat maps and cluster map. The cluster map presents how
materials cluster on the 2D mesh, and heatmaps relate theirproperties to it; hence, correlations among properties can bedirectly observed. This is a human intuitive way to observe highdimensional features while their topology is relatively wellpreserved. This has been demonstrated in Qian et al.’s work.12
3.2. Projection Maps. In order to study the correlation
between the PCE performance and other factors, such as

patterns of distribution, indicating a positive correlation
between them.
Comparing the PCE values projected on the cluster map in
Figure 4 with the other heat maps, we ﬁnd that some materials
with high molecular weight have PCE ranging between 4.21%
and 6.30%, but in general, these high molecular weight polymers
are not within the high PCE regions. This observation is notconsistent with the general consensus that high-performance
OPV materials have high MW due to lower density ofrecombination centers (persistent radical defects revealed by
EPR spectroscopy) and better photoactive layer morphology inthe samples.44 This may be an indication that molecular weight
has competing contributions in getting high PCE values as someother molecular descriptors. This does not exclude thepossibility that molecular weight can play an important role inachieving high PCE. Some research showed that even though
the bandgap or energy levels might not signiﬁcantly change, the12876

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ArticleFigure 7. Presence of atoms for the following elements: N, O, S. PCE boundaries are shown on the cluster maps in red and blue lines.

Figure 8. Information projected cluster map of (a) the number of F atoms contained in the monomers of the donor materials and (b) the existence of 1
or 2 F atoms in the monomers of the donor materials. PCE boundaries are shown on the cluster maps in red and blue lines.

materials obtain a high PCE value, high Eg is necessary.
However, this does not conclude that high Eg leads to high PCEvalue. As shown in Figure 6b, on the contrary, very few high Eg
polymers existing in the high PCE region, and we can see most ofthem are polymers with high Eloss.
3.2.2. Projection of Chemical Elements. Next, we study how
the chemical elements inﬂuence on a trend in the PCE
performance by projecting several elements, including F, N, O,
and S, to the cluster map. From the results shown in Figures 7
and 8, we ﬁnd that only the presence of F has evident patterns of
the distribution on the map, while the presence of N, O, and S
elements in the polymers shows little speciﬁc distribution on themap, indicating that those elements do not decisively inﬂuence
the PCE value. In Figure 8, we also classify the polymers basedon the number of the F atoms in the polymer chain and ﬁnd thatthe distribution of the polymers that contain 1 to 2 F atomsaligns well with the high-PCE region, but the polymers withnone or more than 2 F atoms do not show this pattern. Given
this insight from the ML results, among the total of 253 low PCE
materials, on the basis of Figure 8b, only 14 of them have 1 or 2
ﬂuorine atoms attached in the monomers. We did some further
data analyses and found that 21% of materials in our data set
contain F atoms, but 44% of high-PCE materials have F atoms intheir backbone structure, and among them more than 90% have
1 or 2 F atoms, which constitute 40% of high PCE materials.
The importance of the F functional group in a donor material
is believed to be related to its electron-withdrawing nature.54
Introduction of ﬂuorine into the polymer backbones cansimultaneously downshift HOMO and LUMO levels withoutchemical information, and geometrical information on thepolymers, we now utilize the projection function developed in
SOM on the cluster map. All the projection maps have a highPCE region outlined so that the correlation can be identiﬁed
visually.
3.2.1. Projection of Eg and Eloss. First, we study thecorrelation of PCE values with Eg and Eloss. Note that thesetwo properties are included in the data set but not used in the
training set. Figure 6a shows the distribution of Eloss across thecluster map. It is found that materials with high Eloss values tend
to be aggregated around the low-PCE regions, while the very few
materials with low Eloss (as displayed in Figure 6c) all exist in thehigh PCE value region of the map. This observation is consistent
with recent studies of the OPV materials48−50 that a low Eloss(less than 0.5 eV) can improve the PCE value of the material by
as much as 15% even though most materials typically hold Elossvalues between 0.7 and 1 eV. Figure 6c lists the materials with a
low bandgap and low energy loss. Using the unique ID of eachpolymer in SOM input, we can easily track these polymers to its
original source publications.51−53
There are also a few high Eloss materials in the high-PCE
region, which we mark with rectangles in Figure 6a. By
comparing Eloss and Eg projected cluster maps (i.e., Figure6a,b), we ﬁnd that these materials also have wide bandgap values(high Eg). According to eq 1, high Eloss and high Eg might resultin high Voc, which increases the PCE value. By comparing withthe heatmap of Voc in Figure 5, we conﬁrmed that all these low
Eloss and wide-bandgap OPV materials (WBG) listed in Figure
6d have high Voc values. It brings to light that for high Eloss
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ArticleFigure 9. Information projected cluster map of (a) total number of all aromatic rings (naRing), (b) the ratio between naRing and nChain (naRing/
nChain), (c) the number of ﬁve-membered aromatic rings (n5aRing), and (d) the number of six-membered aromatic rings (n6aRing) on the cluster
map. PCE boundaries are shown on maps in red line.

causing strong steric hindrance of the resulting molecules,54
thereby increasing Voc. Enhancement of the inter/intramolecular interactions and localization of the LUMO density
on the structure are known to increase the crystallinity,
facilitating charge transfer and transport in ﬂuorinatedmolecules.54−57 Even though our data science approach does
not give an explanation why polymers with 1 or 2 F atoms show
better OPV properties, we statistically demonstrate that amajority of polymers containing 1 or 2 F atoms in the monomers
have high PCE values when used in fullerene acceptor devices,which can provide some insight for researchers into engineering
better OPV materials.
In the original data set, there are several pairs of comparisonsthat show the incorporation of ﬂuorine can increase theperformance: PTBF058 and PTBF158 (increase the PCE from
2.7% to 6.2%), PBT-0F59 and PBT-3F59 (increase the PCE from
4.5% to 8.6%), and PBnDT-HTAZ60 and PBnDT-FTAZ60(increase the PCE from 4.36% to 7.1%). However, it is not auniversal rule that the addition of ﬂuorine will increase theperformance. The addition of ﬂuorine could cause the change ofother conditions, thus leading to the decrease of performance.61
3.2.3. Projection of Number of Aromatic Rings. Generalexistence of S shown in Figure 7 suggests the existence of
thiophene rings inside of the polymers. Therefore, we projectsome geometrical information on the monomers on the cluster
map in search for more patterns for high PCE polymers. Figure 9
presents the projection of number of chains, aromatic rings(total, ﬁve-membered and six-membered), and the ratio
between the number of aromatic rings and that of side chains.

The total aromatic rings include all kinds of aromatic ring
structures, and ﬁve-membered or six-membered aromatic ringsin the data set are mostly thiophene and benzene rings.
It has been suggested that the addition of thiophene rings canimprove the ﬁll factor and morphology while designing donor−
acceptor copolymers.54 The prior research shows that the
number of benzenes has a relationship with the performance ofOPV materials.62 This is in fact shown in Figure 9a, where weﬁnd that most of high-PCE materials have 6−9 aromatics rings
within their backbone structures. We therefore projected ﬁvemembered rings (usually thiophene rings) to the cluster map
and then discovered that most of the polymers investigated had
3−7 thiophene rings in their monomer structures. This is clearlyshown in Figure 9c. Even though we did not observe a
correlation between the number of aromatic rings and high PCEvalues, we did expose the trend in current research in this ﬁeld tofocus on polymers containing 3−7 thiophene rings in theirmonomer structures.
The increase of aromatic rings generally leads to a lower
solubility and worse BHJ network, which can be facilitated byincreasing the number of side alkyl chains. The fraction ofnaRing over the nChain represented as naRing/nChain inpercent was projected in the cluster map in Figure 9b. The
results of that projection, however, show little or nocorrespondence with the PCE value distribution, suggestingthat the scaling law is not preserved. Namely, the highperformance polymers with many aromatic rings do not require
many alkyl chains to ensure good solubility, which is aprerequisite for a strong stacking eﬀect and high hole mobility.
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ArticleFigure 10. Information projected cluster map of (a) the number of branched chains, (b) the number of linear chains, and (c) the number of side chainsin the monomer (nChain). PCE boundaries are shown on maps in red lines.

Thus, the nonlinear eﬀect of alkyl chains (branched and linear)and backbone aromatics on the solubility and crystallinity ofpolymers should be clariﬁed in due course.
3.2.4. Projection of Chain Length. For materials that sharethe same backbone structure, the optimal alkyl chain structure
would be a combination of both branched and linear side alkyl
chains that provide improved morphology of the donor−
acceptor mix, thereby leading to better photophysical properties.63−66 Even though it is not meaningful to compare thenumber and length of side chains (linear or branched) acrossmaterials with diﬀerent backbone structures, we are getting
insight into the type of polymers researchers tend to study.
Besides the number of alkyl side chains, the length of eachchain can also be extracted from canonicalized SMILES. We
designed a function to extract the number and length of
backbone and side chains from SMILES by recognizing theconsecutively connected carbon atoms, including their numbers
and lengths. We regard more than 3 carbon atoms as a side chain,
and a side chain with additional attached chains will beconsidered as a branch.
The generated result is the combination of two arrays, such as[0,2,2][8,7,7]; the number of elements in each array represents
the number of chains, and each value in the list represents thelength of each chain. For example, [0,2,2][8,7,7] should beinterpreted as this monomer contains 3 chains, the ﬁrst one is an
8-atom long straight chain, and the second and the third one areboth branched, each with a short side chain of 2 atoms and a long
backbone of 7 atoms. This SMILES parsing algorithm is also
included in the GitHub repo.
With some simple data processing, we can visualize the
distribution of the number of branches and length of chains on
the cluster map by projecting them to the cluster map, as shownin Figure 10. By visually comparing Figure 10a,b, we may
conclude that most of the polymers studied have branched side
chains, whereas there is no obvious preference with respect tothe existence of linear side chains in the materials being studied.
By projecting the number of the side chains to the cluster map(Figure 10c), we found that most materials have 2−4 side chainsattached to the backbone structure in their monomers. Even
though we cannot ﬁnd a pattern for the exact number or lengthof side chains on simple visualizations, at least we discoveredthat the majority of researchers who studied the fullerene OPV
system reported polymers with 2−4 side chains containing atleast one branched side chain. To further study the impact oflength of side chains, speciﬁc studies should be done for eachtype of backbone with varying side chains lengths.
We also studied the average number of heavy atoms on eachside chain’s impact to the properties, as shown in Figure 11.
Most of high-PCE materials have around 7−12 non-hydrogenatoms on each side chain.

Figure 11. Information projected cluster map of average number of
non-hydrogen atoms on each side chain in the monomer.

4. CONCLUSION
We demonstrated that SOM in combination with molecular
descriptor generation techniques can be utilized to study thecorrelation between the structural information on the polymer
materials and the performance of the polymer materials
eﬀectively and eﬃciently. In our case study, by visuallycomparing the PCE cluster maps and projected information,
including other properties, chemical information, and structural
information, we discovered that for fullerene acceptor OPV
systems:
1. The donor polymers with 1 or 2 ﬂuorine atoms in theirmonomer structures tend to have higher PCE values;
more than 90% of polymers with 1 or 2 ﬂuorine atoms
have PCE values higher than 6.3%. Polymers containing
none or more than 2 ﬂuorine atoms do not exhibit thesame pattern of having high PCE values.
2. Molecular weight of the donor polymers does not play adominant role in inﬂuencing the PCE performance when
Mw is compared across all polymers with diﬀerent
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All the data and the algorithm can be found at the GitHub
address: https://github.com/DataScienceUWMSE/SOM_
OPV.

structures, diﬀerent chemical components, and diﬀerent
geometries.
3. Most systems that have low photon energy loss also havehigh PCE values. For high photon energy loss polymers,
only the one with wide bandgap can achieve high PCE.
4. Most donor polymers that are currently under study bymaterials researchers have monomer structures containing branched side chain, and 2−4 side chains. Each side
chain, on average, has 7−12 non-hydrogen atoms on it.

■

ACKNOWLEDGMENTS
A.S. acknowledges the PRESTO program (Grant No.
JPMJPR15N6) from the Japan Science and Technology Agency(JST) and the Japan Society for the Promotion of Science(JSPS) with the KAKENHI Grant-in-Aid for Scientiﬁc Research(A) (Grant No. JP16H02285). The authors also acknowledge
the vitally important encouragement and support made throughthe University of Washington-Tohoku University: Academic
Open Space (UW-TU: AOS). Special thanks to Prof. Samson A.
Jenekhe (UW-ChemE) for mentoring JZ for his Master of
Science degree.

Even though these results were achieved purely from astatistical approach using data science methods, they are
consistent with physical approach research results. Therefore,
we can conclude that our proposed data science workﬂow, or
pipeline, can be successfully utilized to extract useful
information even from data sets containing data coming froma variety of sources across diﬀerent research laboratories.
Although our case study focuses on the fullerene acceptor
system, the proposed study workﬂow we introduced is genericand can be utilized to study other systems, such as nonfullerene
small molecule acceptor organic solar cells systems, or otherresearch topics related to polymer materials. As the amount ofdata available for polymer materials increases, such dataprocessing pipelines as the one we proposed here will become
ever more relevant and will help guide researchers in designing
materials presenting the characteristics they desire.

■

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AUTHOR INFORMATION

Corresponding Authors

Yue Huang − Department of Materials Science and Engineering,
University of Washington, Seattle 98195-1750, Washington,
United States; orcid.org/0000-0002-9927-1243;
Email: huangyue@uw.edu
Fumio S. Ohuchi − Department of Materials Science andEngineering, University of Washington, Seattle 98195-1750,
Washington, United States; School of Engineering, Tohoku
University, 980-8579 Sendai, Japan; Email: ohuchi@uw.edu

Authors

Jingtian Zhang − Chemical Engineering Department, Universityof Washington, Seattle, Wahsington 98195-2120, United States
Edwin S. Jiang − Department of Materials Science andEngineering, University of Washington, Seattle 98195-1750,
Washington, United States
Yutaka Oya − School of Engineering, Tohoku University, 9808579 Sendai, Japan
Akinori Saeki − Department of Applied Chemistry, Graduate
School of Engineering, Osaka University, Suita 565-0871, Osaka,
Japan; orcid.org/0000-0001-7429-2200
Gota Kikugawa − Institute of Fluid Science, Tohuku University,
980-8577 Sendai, Japan
Tomonaga Okabe − Department of Materials Science andEngineering, University of Washington, Seattle 98195-1750,
Washington, United States; School of Engineering, Tohoku
University, 980-8579 Sendai, Japan
Complete contact information is available at:https://pubs.acs.org/10.1021/acs.jpcc.0c00517
Author Contributions#

Y.H. and J.Z. share equal contribution to this project.

Notes

The authors declare no competing ﬁnancial interest.
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