Machine learning (ML) is a field of computer science that uses algorithms and techniques for automating solutions to complex problems that are hard to program using conventional programming methods. ...Owing to the chemical versatility of organic building blocks, a large number of organic semi-conductors have been used for organic solar cells. Selecting a suitable organic semi-conductor is like searching for a needle in a haystack. Data-driven science, the fourth paradigm of science, has the potential to guide experimentalists to discover and develop new high-performance materials. The last decade has seen impressive progress in materials informatics and data science; however, data-driven molecular design of organic solar cell materials is still challenging. The data-analysis capability of machine learning methods is well known. This review is written about the use of machine learning methods for organic solar cell research. In this review, we have outlined the basics of machine learning and common procedures for applying machine learning. A brief introduction on different classes of machine learning algorithms as well as related software and tools is provided. Then, the current research status of machine learning in organic solar cells is reviewed. We have discussed the challenges in anticipating the data driven material design, such as the complexity metric of organic solar cells, diversity of chemical structures and necessary programming ability. We have also proposed some suggestions that can enhance the usefulness of machine learning for organic solar cell research enterprises.
In this review, current research status about the machine learning use in organic solar cell research is reviewed. We have discussed the challenges in anticipating the data driven material design.
The power conversion efficiency (PCE) of organic solar cells (OSCs) is increasing continuously, however, commercialization is far from being achieved due to the very high synthetic cost of materials ...and toxic solvents. Poly(3-hexylthiophene) (P3HT) is the cheapest donor, however, its PCE has remained relatively low for a long time. Recently, a PCE of over 9% has been reported. This study was performed with the aim of predicting the performance of P3HT based organic solar cells through statistical data fit, bypassing the complexity of OSC devices. For this purpose, we have used machine learning to explore data from previously reported devices. Molecular descriptors were used to train machine learning models. We have identified the descriptors that have a positive impact on the PCE. Various machine learning models are used to classify non-fullerene acceptors (NFAs) on the basis of their PCEs. We have also developed various regression models to predict the PCE. The support vector machine showed the best predictive capability. Machine learning models were also trained to predict the energy levels. Over 3000 NFAs were designed using high performing and easily synthesizable building blocks. For virtual screening of new molecules, energy levels were predicted through the already trained model. Acceptors with suitable energy levels matching with those of P3HT were selected. The PCEs of these selected acceptors were also predicted. 87 acceptors with >7.5% PCE were selected. Green solvents were selected on the basis of Hansen solubility parameters predicted using machine learning. Green solvent-containing organic solar cells have better scope of commercialization due to their environment-friendly nature. This study will pave the way for the cheap and fast design of materials for efficient P3HT-based organic solar cells. This study will help to select potential candidates and speed up the breakthroughs.
A time and money efficient machine learning assisted design of non-fullerene small molecule acceptors for P3HT based organic solar cells is reported. Green solvents are also selected using machine learning predicted Hansen solubility parameters.
Solubility plays a critical role in many aspects of research (drugs to materials). Solubility parameters are very useful for selecting appropriate solvents/non-solvents for various applications. In ...the present study, Hansen solubility parameters are predicted using machine learning. More than 40 machine models are tried in the search for the best model. Molecular descriptors and fingerprints are used as inputs to get a comparative view. Machine learning models trained using molecular descriptors have shown higher prediction ability than the model trained using molecular fingerprints. Machine learning models trained using molecular descriptors have shown their potential to be easy and fast models compared to the density functional theory (DFT)/thermodynamic approach. Machine learning creates a "black box" connection to the properties. Therefore, minimal computational cost is required. With the help of the best-trained machine learning model, green solvents are selected for small molecule donors that are used in organic solar cells. Our introduced framework can help to select solvents for organic solar cells in an easy and fast way.
A fast machine learning based framework is introduced for the prediction of solubility parameters and selection of green solvents for small molecular donor-based organic solar cells.
Scintillation based X-ray detection has received great attention for its application in a wide range of areas from security to healthcare. Here, we report highly efficient X-ray scintillators with ...state-of-the-art performance based on an organic metal halide, ethylenebis-triphenylphosphonium manganese (II) bromide ((C
H
P
)MnBr
), which can be prepared using a facile solution growth method at room temperature to form inch sized single crystals. This zero-dimensional organic metal halide hybrid exhibits green emission peaked at 517 nm with a photoluminescence quantum efficiency of ~ 95%. Its X-ray scintillation properties are characterized with an excellent linear response to X-ray dose rate, a high light yield of ~ 80,000 photon MeV
, and a low detection limit of 72.8 nGy s
. X-ray imaging tests show that scintillators based on (C
H
P
)MnBr
powders provide an excellent visualization tool for X-ray radiography, and high resolution flexible scintillators can be fabricated by blending (C
H
P
)MnBr
powders with polydimethylsiloxane.
Chemical structure of small molecule acceptors determines their performance in organic solar cells. Multiscale simulations are necessary to avoid trial‐and‐error based design, ultimately to save time ...and resources. In current study, the effect of sp2‐hybridized nitrogen substitution at the inner or the outmost position of central core, side chain, and terminal group of small molecule acceptors is investigated using multiscale computational modelling. Quantum chemical analysis is used to study the electronic behavior. Nitrogen substitution at end‐capping has significantly decreased the electron‐reorganization energy. No big change is observed in transfer integral and excited state behavior. However, nitrogen substitution at terminal group position is good way to improve electron‐mobility. Power conversion efficiency (PCE) of newly designed acceptors is predicted using machine learning. Molecular dynamics simulations are also performed to explore the dynamics of acceptor and their blends with PBDB‐T polymer donor. Florgy‐Huggins parameter is calculated to study the mixing of designed small molecule acceptors with PBDB‐T. Radial distribution function has indicated that PBDB‐T has a closer packing with N3 and N4. From all analysis, it is found that nitrogen substitution at end‐capping group is a better strategy to design efficient small molecule acceptors.
Multidimensional modelling is performed to envision the structural changes at atomic and molecular level. sp2‐hybridized nitrogen is substituted at the inner or the outmost position of central core, side chain, and terminal group of ITIC for improving the device performance. It is found that nitrogen substitution at end‐capping group is a better strategy to design efficient small molecule acceptors.
Organic solar cells are the most promising candidates for future commercialization. This goal can be quickly achieved by designing new materials and predicting their performance without ...experimentation to reduce the number of potential targets. We introduce a multidimensional design and discovery pipeline to systematize materials discovery and reduce the dependence on a serendipitous approach. Machine learning models are trained on data collected from the literature for the prediction of various properties, such energy levels (HOMO and LUMO), UV/visible absorption maxima (both in solution and film) and power conversion efficiency (PCE). More than 5000 new small molecule acceptors (SMAs) are designed using easily synthesizable building blocks. 1700 small molecule acceptors without a suitable energy level match with PBT7-Th are filtered off. SMAs with blue-shifted absorption maxima are not further considered. The number of SMAs was reduced to 2350 on the basis of the predicted UV/visible absorption maxima. Then, the SMAs were further screened on the basis of the predicted power conversion efficiency (PCE). More than 100 SMAs with PCE >13% were selected and further studied using molecular dynamics (MD) simulations. The mixing behavior of the PBT7-Th:SMA blends was studied using the Flory–Huggins parameter. 15 SMAs that exhibited balanced mixing with PBT7-Th were selected. Finally, the best predicted PCE is over 15% with the common IDTT core, which is far better than that of reported results. This is an effective pipeline to design and screen potential materials without revisiting the previous experimental work, and thus will pave the way to cost- and time-efficient discovery of novel materials.
Abstract
We have investigated the systematic differences introduced when performing a Bayesian-inference analysis of the equation of state (EOS) of neutron stars employing either variable- or ...constant-likelihood functions. The former has the advantage of retaining the full information on the distributions of the measurements, making exhaustive usage of the data. The latter, on the other hand, has the advantage of a much simpler implementation and reduced computational costs. In both approaches, the EOSs have identical priors and have been built using the sound speed parameterization method so as to satisfy the constraints from X-ray and gravitational waves observations, as well as those from chiral effective theory and perturbative quantum chromodynamics. In all cases, the two approaches lead to very similar results and the 90% confidence levels essentially overlap. Some differences do appear, but in regions where the probability density is extremely small and are mostly due to the sharp cutoff on the binary tidal deformability
Λ
˜
≤
720
set in the constant-likelihood approach. Our analysis has also produced two additional results. First, an inverse correlation between the normalized central number density,
n
c
,TOV
/
n
s
, and the radius of a maximally massive star,
R
TOV
. Second, and most importantly, it has confirmed the relation between the chirp mass and the binary tidal deformability. The importance of this result is that it relates
chirp
, which is measured very accurately, and
Λ
˜
, which contains important information on the EOS. Hence, when
chirp
is measured in future detections, our relation can be used to set tight constraints on
Λ
˜
.
In recent years, a rapid evolution of organic solar cells (OSCs) has been achieved by virtue of structural design of active layer materials and optimization of film morphology. Along with other ...characterization techniques, grazing incidence small‐ and wide‐angle X‐ray scattering (GISAXS and GIWAXS) have played significant role in deeper understanding of film morphology. Herein, the importance of these techniques is explained with examples from various aspects of OSCs. Different pre‐ and post‐processing conditions such as solvent effect, solvent additive, solvent, and thermal annealing are studied in the framework of these techniques. Moreover, the impact of donor:acceptor ratio and molecular weight of semiconductor on microstructure is also explored. Finally, the effect of chemical structure of organic semiconductors (both polymers and small molecules) on the film morphology is discussed. These techniques provide valuable information about crystallinity, phase separation, and domain size of nanostructured film morphology, which helps to optimize the film morphology and enhances the performance of OSCs. The role of these techniques will become more important as the mystery of film morphology still has to be solved.
Grazing incidence small‐ and wide‐angle X‐ray scattering (GISAXS and GIWAXS) are extensively used for the characterization of film morphology of organic solar cells (OSCs). Herein, the use of these techniques to find the effect of chemistry of active layer materials and different pre‐ and postprocessing conditions on the film morphology of OSCs is discussed.
Herein, we report a theoretical and experimental study of the water‐gas shift (WGS) reaction on Ir1/FeOx single‐atom catalysts. Water dissociates to OH* on the Ir1 single atom and H* on the ...first‐neighbour O atom bonded with a Fe site. The adsorbed CO on Ir1 reacts with another adjacent O atom to produce CO2, yielding an oxygen vacancy (Ovac). Then, the formation of H2 becomes feasible due to migration of H from adsorbed OH* toward Ir1 and its subsequent reaction with another H*. The interaction of Ir1 and the second‐neighbouring Fe species demonstrates a new WGS pathway featured by electron transfer at the active site from Fe3+−O⋅⋅⋅Ir2+−Ovac to Fe2+−Ovac⋅⋅⋅Ir3+−O with the involvement of Ovac. The redox mechanism for WGS reaction through a dual metal active site (DMAS) is different from the conventional associative mechanism with the formation of formate or carboxyl intermediates. The proposed new reaction mechanism is corroborated by the experimental results with Ir1/FeOx for sequential production of CO2 and H2.
Two sites are better than one: A redox mechanism with dual metal active site was found for the water‐gas shift (WGS) reaction on the Ir1/FeOx single‐atom catalyst by theoretical and experimental studies. The Ir1 and Fe atoms jointly facilitate the creation of an oxygen vacancy at the Fe species neighbouring the Ir1 atom, leading to sequential production of CO2 and H2.
Organic metal halide hybrids (OMHHs) have attracted great research attention owing to their exceptional structure and property tunability. Using appropriate organic and inorganic metal halide ...components, OMHHs with controlled dimensionalities at the molecular level, from 3D to 2D, 1D, and 0D structures, can be obtained. In 0D OMHHs, anionic metal halide polyhedrons are surrounded and completely isolated by organic cations to form single crystalline “host–guest” structures. These ionically bonded organic–inorganic hybrid systems often exhibit the intrinsic properties of individual metal halide species, for instance, highly efficient Stokes‐shifted broadband emissions. In this progress report, the recent advances in the development and study of luminescent 0D OMHHs are discussed: from synthetic structural control to fundamental understanding of the structure–property relationship and device integration.
Zero‐dimensional organic metal halide hybrids (0D OMHHs) have emerged as highly promising photoactive hybrid materials with unique properties and applications in a variety of areas. This progress report discusses the recent advances in the development and study of luminescent 0D OMHHs, from synthetic structural control to fundamental understanding of the structure–property relationship and device integration.
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