Computer science implements algorithms and techniques to automate problem-solving solutions. Due to the chemical versatility of organic building blocks, many organic semiconductors have been utilized for organic solar cells (OSCs). The computational methods can potentially drive experimentalists to discover and design high-performance materials. OSCs' objective is the performance of their energy conversion efficiency and stability. One idea that has improved efficiency and stability is that of ternary systems, known as ternary organic solar cells (TOSCs). The photoactive layer in TOSCs is formed by mixing three distinct components together. This review is about the employment of computational approaches for investigating TOSCs. Here, we outlined the basics of computational methods and standard application procedures. This article offers a concise overview of various computational algorithms, relevant software, and tools. Additionally, it examines the present state of research regarding computations in TOSCs. The challenges associated with TOSCs, including intricacy metrics, diverse chemical structures, and programming skills, are discussed. Furthermore, we suggest some ways to improve the utility of computation in TOSCs research enterprises.

1.
R. G.
Kepler
, “
Charge carrier production and mobility in anthracene crystals
,”
Phys. Rev.
119
,
1226
1229
(
1960
).
2.
Q.
An
,
F.
Zhang
,
J.
Zhang
,
W.
Tang
,
Z.
Deng
, and
B.
Hu
, “
Versatile ternary organic solar cells: A critical review
,”
Energy Environ. Sci.
9
,
281
322
(
2016
).
3.
M.
Günther
,
N.
Kazerouni
,
D.
Blätte
,
J. D.
Perea
,
B. C.
Thompson
, and
T.
Ameri
, “
Models and mechanisms of ternary organic solar cells
,”
Nat Rev Mater.
8
,
456
471
(
2023
).
4.
L.
Lu
,
M. A.
Kelly
,
W.
You
, and
L.
Yu
, “
Status and prospects for ternary organic photovoltaics
,”
Nat. Photonics
9
,
491
500
(
2015
).
5.
X.
Xu
,
Y.
Li
, and
Q.
Peng
, “
Recent advances in morphology optimizations towards highly efficient ternary organic solar cells
,”
Nano Select
1
,
30
58
(
2020
).
6.
T.
Ameri
,
P.
Khoram
,
J.
Min
, and
C. J.
Brabec
, “
Organic ternary solar cells: A review
,”
Adv. Mater.
25
,
4245
4266
(
2013
).
7.
X.
Xu
and
Q.
Peng
, “
Hole/electron transporting materials for nonfullerene organic solar cells
,”
Chem.–A Eur. J.
28
,
e202104453
(
2022
).
8.
T.
Liu
,
R.
Ma
,
Z.
Luo
,
Y.
Guo
,
G.
Zhang
,
Y.
Xiao
,
T.
Yang
,
Y.
Chen
,
G.
Li
,
Y.
Yi
,
X.
Lu
,
H.
Yan
, and
B.
Tang
, “
Concurrent improvement in JSC and VOC in high-efficiency ternary organic solar cells enabled by a red-absorbing small-molecule acceptor with a high LUMO level
,”
Energy Environ. Sci.
13
,
2115
2123
(
2020
).
9.
Y.
Tang
,
J.
Li
,
P.
Du
,
H.
Zhang
,
C.
Zheng
,
H.
Lin
,
X.
Du
, and
S.
Tao
, “
Fullerene's ring: A new strategy to improve the performance of fullerene organic solar cells
,”
Org. Electron.
83
,
105747
(
2020
).
10.
J.-W.
Ha
,
C. E.
Song
,
H. S.
Kim
,
D. H.
Ryu
,
W. S.
Shin
, and
D.-H.
Hwang
, “
Highly efficient and photostable ternary organic solar cells enabled by the combination of non-fullerene and fullerene acceptors with thienopyrrolodione-based polymer donors
,”
ACS Appl. Mater. Interfaces
12
,
51699
51708
(
2020
).
11.
T.
Liu
,
Y.
Guo
,
Y.
Yi
,
L.
Huo
,
X.
Xue
,
X.
Sun
,
H.
Fu
,
W.
Xiong
,
D.
Meng
,
Z.
Wang
,
F.
Liu
,
T. P.
Russell
, and
Y.
Sun
, “
Ternary organic solar cells based on two compatible nonfullerene acceptors with power conversion efficiency >10%
,”
Adv. Mater.
28
,
10008
10015
(
2016
).
12.
L.
Huo
,
T.
Liu
,
X.
Sun
,
Y.
Cai
,
A. J.
Heeger
, and
Y.
Sun
, “
Single-junction organic solar cells based on a novel wide-bandgap polymer with efficiency of 9.7%
,”
Adv. Mater.
27
,
2938
2944
(
2015
).
13.
D.
Meng
,
D.
Sun
,
C.
Zhong
,
T.
Liu
,
B.
Fan
,
L.
Huo
,
Y.
Li
,
W.
Jiang
,
H.
Choi
,
T.
Kim
,
J. Y.
Kim
,
Y.
Sun
,
Z.
Wang
, and
A. J.
Heeger
, “
High-performance solution-processed non-fullerene organic solar cells based on selenophene-containing perylene bisimide acceptor
,”
J. Am. Chem. Soc.
138
,
375
380
(
2016
).
14.
Y.
Lin
,
F.
Zhao
,
Q.
He
,
L.
Huo
,
Y.
Wu
,
T. C.
Parker
,
W.
Ma
,
Y.
Sun
,
C.
Wang
,
D.
Zhu
,
A. J.
Heeger
,
S. R.
Marder
, and
X.
Zhan
, “
High-performance electron acceptor with thienyl side chains for organic photovoltaics
,”
J. Am. Chem. Soc.
138
,
4955
4961
(
2016
).
15.
W.
Liu
,
Q.
Liu
,
C.
Xiang
,
H.
Zhou
,
L.
Jiang
, and
Y.
Zou
, “
Theoretical exploration of optoelectronic performance of PM6:Y6 series-based organic solar cells
,”
Surf. Interfaces
26
,
101385
(
2021
).
16.
T.
Zhu
,
L.
Shen
,
S.
Xun
,
J. S.
Sarmiento
,
Y.
Yang
,
L.
Zheng
,
H.
Li
,
H.
Wang
,
J.-L.
Bredas
, and
X.
Gong
, “
High‐performance ternary perovskite–organic solar cells
,”
Adv. Mater.
34
(
13
),
2109348
(
2022
).
17.
R.
Sun
,
T.
Wang
,
Q.
Fan
,
M.
Wu
,
X.
Yang
,
X.
Wu
,
Y.
Yu
,
X.
Xia
,
F.
Cui
,
J.
Wan
,
X.
Lu
,
X.
Hao
,
A. K.-Y.
Jen
,
E.
Spiecker
, and
J.
Min
, “
18.2%-efficient ternary all-polymer organic solar cells with improved stability enabled by a chlorinated guest polymer acceptor
,”
Joule
7
(
1
),
221
237
(
2023
).
18.
T.
Segaran
,
Programming Collective Intelligence: Building Smart Web 2.0 Applications
(
O'Reilly Media, Inc
.,
2007
).
19.
J. A.
Keith
,
V.
Vassilev-Galindo
,
B.
Cheng
,
S.
Chmiela
,
M.
Gastegger
,
K.-R.
Müller
, and
A.
Tkatchenko
, “
Combining machine learning and computational chemistry for predictive insights into chemical systems
,”
Chem. Rev.
121
,
9816
9872
(
2021
).
20.
B.
Cheng
,
R.-R.
Griffiths
,
S.
Wengert
,
C.
Kunkel
,
T.
Stenczel
,
B.
Zhu
,
V. L.
Deringer
,
N.
Bernstein
,
J. T.
Margraf
, and
K.
Reuter
, “
Mapping materials and molecules
,”
Acc. Chem. Res.
53
,
1981
1991
(
2020
).
21.
A.
Reinhardt
,
C. J.
Pickard
, and
B.
Cheng
, “
Predicting the phase diagram of titanium dioxide with random search and pattern recognition
,”
Phys. Chem. Chem. Phys.
22
,
12697
12705
(
2020
).
22.
P.
Leinen
,
M.
Esders
,
K. T.
Schütt
,
C.
Wagner
,
K.-R.
Müller
, and
F. S.
Tautz
, “
Autonomous robotic nanofabrication with reinforcement learning
,”
Sci. Adv.
6
,
eabb6987
(
2020
).
23.
R. S.
Sutton
and
A. G.
Barto
,
Reinforcement Learning: An Introduction
(
MIT Press
,
2018
).
24.
S.
Nagasawa
,
E.
Al-Naamani
, and
A.
Saeki
, “
Computer-aided screening of conjugated polymers for organic solar cell: Classification by random forest
,”
J. Phys. Chem. Lett.
9
,
2639
2646
(
2018
).
25.
H.
Sahu
,
W.
Rao
,
A.
Troisi
, and
H.
Ma
, “
Toward predicting efficiency of organic solar cells via machine learning and improved descriptors
,”
Adv. Energy Mater.
8
,
1801032
(
2018
).
26.
B.
Cao
,
L. A.
Adutwum
,
A. O.
Oliynyk
,
E. J.
Luber
,
B. C.
Olsen
,
A.
Mar
, and
J. M.
Buriak
, “
How to optimize materials and devices via design of experiments and machine learning: Demonstration using organic photovoltaics
,”
ACS Nano
12
,
7434
7444
(
2018
).
27.
D.
Padula
,
J. D.
Simpson
, and
A.
Troisi
, “
Combining electronic and structural features in machine learning models to predict organic solar cells properties
,”
Mater. Horiz.
6
,
343
349
(
2019
).
28.
H.
Sahu
,
F.
Yang
,
X.
Ye
,
J.
Ma
,
W.
Fang
, and
H.
Ma
, “
Designing promising molecules for organic solar cells via machine learning assisted virtual screening
,”
J. Mater. Chem. A
7
,
17480
17488
(
2019
).
29.
M.
Lee
, “
Insights from machine learning techniques for predicting the efficiency of fullerene derivatives‐based ternary organic solar cells at ternary blend design
,”
Adv. Energy Mater.
9
,
1900891
(
2019
).
30.
D.
Padula
and
A.
Troisi
, “
Concurrent optimization of organic donor–acceptor pairs through machine learning
,”
Adv. Energy Mater.
9
,
1902463
(
2019
).
31.
Z.
Li
,
Q.
Xu
,
Q.
Sun
,
Z.
Hou
, and
W.
Yin
, “
Thermodynamic stability landscape of halide double perovskites via high‐throughput computing and machine learning
,”
Adv. Funct. Mater.
29
,
1807280
(
2019
).
32.
Y.
Yu
,
X.
Tan
,
S.
Ning
, and
Y.
Wu
, “
Machine learning for understanding compatibility of organic–inorganic hybrid perovskites with post-treatment amines
,”
ACS Energy Lett.
4
,
397
404
(
2019
).
33.
J.
Im
,
S.
Lee
,
T.-W.
Ko
,
H. W.
Kim
,
Y.
Hyon
, and
H.
Chang
, “
Identifying Pb-free perovskites for solar cells by machine learning
,”
npj Comput Mater.
5
,
37
(
2019
).
34.
J. S.
Rocha‐Ortiz
,
J.
Wu
,
J.
Wenzel
,
A. J.
Bornschlegl
,
J. D.
Perea
,
S.
Leon
,
A.
Barabash
,
A.‐S.
Wollny
,
D. M.
Guldi
,
J.
Zhang
,
A.
Insuasty
,
L.
Lüer
,
A.
Ortiz
,
A.
Hirsch
,
C. J.
Brabec
, “
Enhancing planar inverted perovskite solar cells with innovative dumbbell‐shaped HTMs: A study of hexabenzocoronene and pyrene‐BODIPY‐triarylamine derivatives
,”
Adv. Funct. Mater Adv.,
2304262
(
2023
).
35.
M.-H.
Lee
, “
A machine learning-based design rule for improved open‐circuit voltage in ternary organic solar cells
,”
Adv. Intell. Syst.
2
,
1900108
(
2020
).
36.
A.
Mahmood
and
J.-L.
Wang
, “
Machine learning for high performance organic solar cells: Current scenario and future prospects
,”
Energy Environ. Sci.
14
,
90
105
(
2021
).
37.
M.
Lee
, “
Machine learning for understanding the relationship between the charge transport mobility and electronic energy levels for n‐type organic field‐effect transistors
,”
Adv. Electron. Mater.
5
,
1900573
(
2019
).
38.
T.
Wang
,
C.
Zhang
,
H.
Snoussi
, and
G.
Zhang
, “
Machine learning approaches for thermoelectric materials research
,”
Adv. Funct. Mater.
30
,
1906041
(
2020
).
39.
R.
Hu
,
J.
Song
,
Y.
Liu
,
W.
Xi
,
Y.
Zhao
,
X.
Yu
,
Q.
Cheng
,
G.
Tao
, and
X.
Luo
, “
Machine learning-optimized Tamm emitter for high-performance thermophotovoltaic system with detailed balance analysis
,”
Nano Energy
72
,
104687
(
2020
).
40.
A. H.
Vo
,
T. R.
van Vleet
,
R. R.
Gupta
,
M. J.
Liguori
, and
M. S.
Rao
, “
An overview of machine learning and big data for drug toxicity evaluation
,”
Chem. Res. Toxicol.
33
,
20
37
(
2019
).
41.
B.
Sanchez-Lengeling
and
A.
Aspuru-Guzik
, “
Inverse molecular design using machine learning: Generative models for matter engineering
,”
Science
361
,
360
365
(
2018
).
42.
J. D.
Perea
,
S.
Langner
,
M.
Salvador
,
B.
Sanchez-Lengeling
,
N.
Li
,
C.
Zhang
,
G.
Jarvas
,
J.
Kontos
,
A.
Dallos
, and
A.
Aspuru-Guzik
, “
Introducing a new potential figure of merit for evaluating microstructure stability in photovoltaic polymer-fullerene blends
,”
J. Phys. Chem. C
121
,
18153
18161
(
2017
).
43.
N.
Gasparini
,
S.
Kahmann
,
M.
Salvador
,
J. D.
Perea
,
A.
Sperlich
,
A.
Baumann
,
N.
Li
,
S.
Rechberger
,
E.
Spiecker
, and
V.
Dyakonov
, “
Favorable mixing thermodynamics in ternary polymer blends for realizing high efficiency plastic solar cells
,”
Adv. Energy Mater.
9
,
1803394
(
2019
).
44.
X.
Du
,
X.
Jiao
,
S.
Rechberger
,
J. D.
Perea
,
M.
Meyer
,
N.
Kazerouni
,
E.
Spiecker
,
H.
Ade
,
C. J.
Brabec
,
R. J.
Fink
, and
T.
Ameri
, “
Crystallization of sensitizers controls morphology and performance in Si-/C-PCPDTBT-sensitized P3HT:ICBA ternary blends
,”
Macromolecules
50
(
6
),
2415
2423
(
2017
).
45.
P. B.
Jørgensen
,
M.
Mesta
,
S.
Shil
,
J. M.
García Lastra
,
K. W.
Jacobsen
,
K. S.
Thygesen
, and
M. N.
Schmidt
, “
Machine learning-based screening of complex molecules for polymer solar cells
,”
J. Chem. Phys.
148
,
241735
(
2018
).
46.
Q.
Yue
,
W.
Liu
, and
X.
Zhu
, “
n-Type molecular photovoltaic materials: Design strategies and device applications
,”
J. Am. Chem. Soc.
142
,
11613
11628
(
2020
).
47.
J.
Gao
,
W.
Gao
,
X.
Ma
,
Z.
Hu
,
C.
Xu
,
X.
Wang
,
Q.
An
,
C.
Yang
,
X.
Zhang
, and
F.
Zhang
, “
Over 14.5% efficiency and 71.6% fill factor of ternary organic solar cells with 300 nm thick active layers
,”
Energy Environ. Sci.
13
,
958
967
(
2020
).
48.
Z.
Zhou
,
S.
Xu
,
J.
Song
,
Y.
Jin
,
Q.
Yue
,
Y.
Qian
,
F.
Liu
,
F.
Zhang
, and
X.
Zhu
, “
High-efficiency small-molecule ternary solar cells with a hierarchical morphology enabled by synergizing fullerene and non-fullerene acceptors
,”
Nat. Energy
3
,
952
959
(
2018
).
49.
F.
Kaka
,
M.
Keshav
, and
P. C.
Ramamurthy
, “
Optimising the photovoltaic parameters in donor–acceptor–acceptor ternary polymer solar cells using Machine Learning framework
,”
Sol. Energy
231
,
447
457
(
2022
).
50.
D.
Huang
,
Z.
Li
,
K.
Wang
,
H.
Zhou
,
X.
Zhao
,
X.
Peng
,
R.
Zhang
,
J.
Wu
,
J.
Liang
, and
L.
Zhao
, “
Probing the effect of photovoltaic material on Voc in ternary polymer solar cells with non-fullerene acceptors by machine learning
,”
Polymers
15
(
13
),
2954
(
2023
).
51.
D.
Huang
,
K.
Wang
,
Z.
Li
,
H.
Zhou
,
X.
Zhao
,
X.
Peng
,
J.
Wu
,
J.
Liang
,
J.
Meng
, and
L.
Zhao
, “
A machine learning prediction model for quantitative analyzing the influence of non-radiative voltage loss on non-fullerene organic solar cells
,”
Chem. Eng. J.
475
,
145958
(
2023
).
52.
C.
Caddeo
,
J.
Ackermann
, and
A.
Mattoni
, “
A theoretical perspective on the thermodynamic stability of polymer blends for solar cells: From experiments to predictive modeling
,”
Sol. RRL
6
(
9
),
2200172
(
2022
).
53.
P.
Malhotra
,
K.
Khandelwal
,
S.
Biswas
,
F. C.
Chen
, and
G. D.
Sharma
, “
Opportunities and challenges for machine learning to select combination of donor and acceptor materials for efficient organic solar cells
,”
J. Mater. Chem. C
10
(
47
),
17781
17811
(
2022
).
54.
M. H.
Lee
, “
Identifying correlation between the open-circuit voltage and the frontier orbital energies of non-fullerene organic solar cells based on interpretable machine-learning approaches
,”
Sol. Energy
234
,
360
367
(
2022
).
55.
M. H.
Lee
, “
Interpretable machine learning model for the highly accurate prediction of efficiency of ternary organic solar cells based on nonfullerene acceptor using effective molecular descriptors
,”
Sol. RRL
7
(
14
),
2300307
(
2023
).
56.
J. H.
Li
,
C. R.
Zhang
,
M. L.
Zhang
,
X. M.
Liu
,
J. J.
Gong
,
Y. H.
Chen
,
Z. J.
Liu
,
Y. Z.
Wu
, and
H. S.
Chen
,
Machine Learning Study of D:A1:A2 Ternary Organic Solar Cells
(SSRN Library, 2023).
57.
R.
Suthar
,
T.
Abhijith
,
P.
Sharma
, and
S.
Karak
, “
Machine learning framework for the analysis and prediction of energy loss for non-fullerene organic solar cells
,”
Sol. Energy
250
,
119
127
(
2023
).
58.
C.
Guo
,
Z.
Li
,
K.
Wang
,
X.
Zhou
,
D.
Huang
,
J.
Liang
, and
L.
Zhao
, “
Accelerated exploration of efficient ternary solar cells with PTB7: PC 71 BM: SMPV1 using machine-learning methods
,”
Phys. Chem. Chem. Phys.
24
(
37
),
22538
22545
(
2022
).
59.
C.
Yao
,
X.
Li
,
Y.
Yang
,
L.
Li
,
M.
Bo
,
C.
Peng
, and
J.
Wang
, “
Machine learning with quantum chemistry descriptors: Predicting the solubility of small-molecule optoelectronic materials for organic solar cells
,”
J. Mater. Chem. A
10
(
30
),
15999
16006
(
2022
).
60.
F.
Oviedo
,
D. S.
Hayden
,
T.
Heumeuller
,
J.
Wortmann
,
J. D.
Perea
,
R.
Naik
et al,
DeepDeg: Forecasting and explaining degradation in novel photovoltaics
. chemRxiv (
2023
).
61.
L. M.
Roch
,
F.
Häse
,
C.
Kreisbeck
,
T.
Tamayo-Mendoza
,
L. P. E.
Yunker
,
J. E.
Hein
, and
A.
Aspuru-Guzik
, “
ChemOS: An orchestration software to democratize autonomous discovery
,” chemRxiv (
2018
).
62.
S.
Langner
,
F.
Häse
,
J. D.
Perea
,
T.
Stubhan
,
J.
Hauch
,
L. M.
Roch
,
T.
Heumueller
,
A.
Aspuru-Guzik
, and
C. J.
Brabec
, “
Beyond ternary OPV: High-throughput experimentation and self-driving laboratories optimize multicomponent systems
,”
Adv. Mater.
32
,
1907801
(
2020
).
63.
L. M.
Roch
,
F.
Häse
,
C.
Kreisbeck
,
T.
Tamayo-Mendoza
,
L. P. E.
Yunker
,
J. E.
Hein
, and
A.
Aspuru-Guzik
, “
ChemOS: Orchestrating autonomous experimentation
,”
Sci. Rob.
3
,
eaat5559
(
2018
).
64.
Dassault Systèmes
, see https://www.3ds.com/products-services/biovia/products/molecular-modeling-simulation/solvation-chemistry/biovia-cosmotherm/ for “
Biovia Cosmotherm
, Dassault Systèmes” (last accessed October 20, 2023).
65.
K.
Choudhary
,
K. F.
Garrity
,
A. C. E.
Reid
et al, “
The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design
,”
npj Comput. Mater.
6
,
173
(
2020
).
You do not currently have access to this content.