Machine learning enabled development of unexplored perovskite solar cells with high efficiency

Yan, Wensheng; Liu, Yiming; Zang, Yue; Cheng, Jiahao; Wang, Yu; Chu, Liang; Tan, Xinyu; Liu, Liu; Zhou, Peng; Li, Wangnan; Zhong, Zhicheng

doi:10.1016/j.nanoen.2022.107394

Cited by 38 publications

(27 citation statements)

References 27 publications

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“…As the specific surface area is defined as the total surface area of a substance per unit mass, an increase in the radius of the atom at the Bposition increases the atomic mass, thereby reducing the specific surface area value. The compound LaMg 0.6 Cr 0.4 O 3 (23) has a higher Rb value and a lower specific surface area value than La 0.5 Bi 0.2 Ba 0.2 Mn 0.1 FeO 3 (35) which has a lower Rb value and a higher specific surface area value.…”

Section: Interpretation Of the Descriptorsmentioning

confidence: 96%

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A machine learning q‐RASPR approach for efficient predictions of the specific surface area of perovskites**

Banerjee

Gajewicz-Skretna

Roy

2023

Molecular Informatics

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In this study, the specific surface area of various perovskites was modeled using a novel quantitative read-across structure-property relationship (q-RASPR) approach, which clubs both Read-Across (RA) and quantitative structure-property relationship (QSPR) together. After optimization of the hyper-parameters, certain similarity-based error measures for each query compound were obtained. Clubbing some of these error-based measures with the previously selected features along with the Read-Across prediction function, a number of machine learning models were developed using Partial Least Squares (PLS), ridge regression (RR), linear support vector regression (LSVR), and random forest (RF) regression. Based on the external prediction quality and interpretability, the PLS model was selected as the best predictor which underscored the previously reported results. The finally selected model should efficiently predict specific surface areas of other perovskites for their use in photocatalysis. The new q-RASPR method also appears promising for the prediction of several other property endpoints of interest in materials science.

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Section: Interpretation Of the Descriptorsmentioning

confidence: 96%

“…adopted ML approaches on lead iodide-based perovskites to predict their dimensionality. Yan et al [23]. also used ML approaches to predict five unexplored perovskites with low bandgap, short circuit current density, and open circuit voltage for the design of highly efficient perovskite solar cells.…”

Section: Introductionmentioning

confidence: 99%

A machine learning q‐RASPR approach for efficient predictions of the specific surface area of perovskites**

Banerjee

Gajewicz-Skretna

Roy

2023

Molecular Informatics

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“…Nowadays, machine learning is used in the elds of energy materials and solar cells to map the correlations between variables and the results; it can learn from past results and provide fast predictions of unknown results. It has been used to understand perovskite properties based on the structures/ compositions and to develop new perovskites, 22,23 select the capping layer for perovskite lms, 24 explore suitable perovskites for use in highly efficient PSCs, 25 screen candidates for use in organic solar cells, 26 and identify the key factors governing the device performances of Cu(In,Ga)Se 2 solar cells. 27 These previous attempts reveal the power of machine learning in processing the complex relationships between materials and material/device performance.…”

Section: Introductionmentioning

confidence: 99%

Screening interface passivation materials intelligently through machine learning for highly efficient perovskite solar cells

Liu

Wei

et al. 2022

J. Mater. Chem. A

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“…[17] Yan et al demonstrated a strategy and applicability to boost unknown perovskite for solar cell application by combining approaches of ML and light management. [18] Small molecules-based HTMs are considered potential alternatives for Spiro-OMeTAD and are extensively developed through a molecular engineering approach, are costeffective, with an opportunity to tune the electro-optical properties and energy level. In this work, we computed a series of HTMs having metal phthalocyanines (MPcs, M = Zn or Cu), pyridine, and substituted phenothiazine core.…”

Section: Introductionmentioning

confidence: 99%

“…[ 19 ] Zhong et al demonstrated a strategy and applicability to boost unknown perovskite for solar cell application by combining approaches of ML and light management. [ 20 ]…”

Section: Introductionmentioning

confidence: 99%

Automated Machine Learning Approach in Material Discovery of Hole Selective Layers for Perovskite Solar Cells

et al. 2022

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In the rapidly emerging field of perovskite solar cells, rational hole selective layer development is considered a double-engine of this progress. To tap into the full potential and accelerate the commercialization path, Machine Learning (ML) is being tasked for perovskite screening and on organic semiconductors core group based on phthalocyanines, pyridine, triazatruxene, thiophenes, carbazole, and phenothiazine to yield efficient solar cells. However, sincere efforts have not led to the design of hole selective layers. Here we demonstrate how ML can be applied to the advancement of hole transport materials (HTMs) to accelerate the development. We evaluated the influence of various HTMs with various groups on the optoelectronic features and photovoltaic performance and validated it using both the random forest model and AutoML framework General Automated Machine Learning Assistant (GAMA). To this end, we utilized the GAMA to predict the suitability of hole selective layers and it returned a 0.0542 ± 0.0470 RMSE score for 15 different materials on average. We (i) established correlations between experimental and predicted results; and (ii) implemented GAMA for HTM suitability prediction. This paves the way for judicious and effective ways to overcome the bottleneck in the development of HTMs. In particular, the prediction approach on optoelectronic features and photovoltaic performance from GAMA is an effective, reliable, and fast methodology and is pioneering in the field of hole transport material screening.

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Machine learning enabled development of unexplored perovskite solar cells with high efficiency

Cited by 38 publications

References 27 publications

A machine learning q‐RASPR approach for efficient predictions of the specific surface area of perovskites**

A machine learning q‐RASPR approach for efficient predictions of the specific surface area of perovskites**

Screening interface passivation materials intelligently through machine learning for highly efficient perovskite solar cells

Automated Machine Learning Approach in Material Discovery of Hole Selective Layers for Perovskite Solar Cells

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