Machine-learning-assisted discovery of perovskite materials with high dielectric breakdown strength

Li, Jianbo; Peng, Yuzhong; Zhao, Lupeng; Chen, Guodong; Zeng, Li; Wei, Guoqiang; Xu, Yanhua

doi:10.1039/d2ma00839d

Cited by 8 publications

(4 citation statements)

References 48 publications

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“…To reduce the effect of random segmentation of the data set on the model’s performance, traversal operation on the segmentation percentage of the data set is done . The test set size range is selected from 10 to 95%, and the step size is 5%.…”

Section: Resultsmentioning

confidence: 99%

“…To reduce the effect of random segmentation of the data set on the model's performance, traversal operation on the segmentation percentage of the data set is done. 35 The test set size range is selected from 10 to 95%, and the step size is 5%. The WAE model is again trained on the data set, and by evaluating the metrics scores, it is concluded that a test size equal to 10% outperformed all the other test sizes, and the R 2 score is improved to 0.85.…”

Section: Training and Testing Of The Weighted Ensemble Averaging Modelmentioning

confidence: 99%

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A Machine Learning Aided Yield Prediction Model for the Preparation of Cellulose Nanocrystals

Sreedev,

Kurukkal Balakrishnan,

Kalarikkal

2024

ACS Appl. Eng. Mater.

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Machine learning is one of the most innovative tools that has entered the materials science toolkit in recent years. This work employs a machine learning strategy to develop a yield prediction model for producing cellulose nanocrystals (CNCs). It analyses the critical factors affecting the yield from CNCs by optimizing reaction conditions and reducing experiments. First, a data set of CNCs is established, including cellulose sources and reaction conditions. The Weighted Average Ensemble (WAE) approach is applied to an ensemble of five tree-based base models on the data set, and it was found that the WAE surpasses all the base models. The impact of critical features on yield prediction is analyzed with partial dependence plots and individual conditional expectation plots. Batch experiments are mainly used to produce CNCs, but these are time-consuming. In this context, the WAE model is a promising tool for rapidly predicting the yield, and this study provides an excellent gateway to improve the extraction of CNCs with high yields.

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Section: Resultsmentioning

confidence: 99%

Section: Training and Testing Of The Weighted Ensemble Averaging Modelmentioning

confidence: 99%

A Machine Learning Aided Yield Prediction Model for the Preparation of Cellulose Nanocrystals

Sreedev,

Kurukkal Balakrishnan,

Kalarikkal

2024

ACS Appl. Eng. Mater.

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“…Predicting the basic physical information in perovskite can be of great help in the exploration of new materials, the mapping of experimental parameters and the understanding of structurefunction relationships. Many models have been developed to predict the physical properties of perovskite such as bandgap 110,119 , oxide ionic conductivity 120 , thermodynamic stability 121,122 , dielectric breakdown strength 123,124 , lattice parameters 125 , crystal structure 126 . For example, Zhang et al establish the lattice constants model based on cubic perovskites 127,128 .…”

Section: Types Of Perovskite Prediction Tasksmentioning

confidence: 99%

Machine learning in energy chemistry: introduction, challenges and perspectives

2023

Energy Adv.

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“…Another work by Q. Tao et al [23] described different ML algorithms to identify different properties of inorganic, hybrid organic-inorganic, and double perovskites to optimize the experiment process during the property's discovery of new materials. Work reported by J. Li et al [24] employed a dataset comprising 760 perovskites to determine the phonon cutoff frequency and applied six distinct ML algorithms to predict this instrumental variable using features available within the provided database.…”

Section: Introductionmentioning

confidence: 99%

Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization

Nguyen,

Kim,

Jung

et al. 2023

Photonics

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Significant focus has been directed towards inorganic perovskite solar cells because of their notable capabilities in converting sunlight to electricity effectively, their efficient light absorption, and their suitability for conventional semiconductor manufacturing methods. The identification of the composition of perovskite materials is an ongoing challenge to achieve high performing solar cells. Conventional methods of trial and error frequently prove insufficient, especially when confronted with a multitude of potential candidates. In response to this challenge, the suggestion is to employ a machine-learning strategy for more precise and efficient prediction of the characteristics of new inorganic perovskite materials. This work utilized a dataset sourced from the Materials Project database, consisting of 1528 ABX3 materials with varying halide elements (X = F, Cl, Br, Se) and information regarding their bandgap characteristics, including whether they are direct or indirect. By leveraging data augmentation and machine learning (ML) techniques along with a collection of established bandgap values and structural attributes, our proposed model can accurately and rapidly predict the bandgap of novel materials, while also identifying the key elements that contribute to this property. This information can be used to guide the discovery of new organic perovskite materials with desirable properties. Six different machine learning algorithms, including Logistic Regression (LR), Multi-layer Perceptron (MLP), Decision Tree (DT), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF), were used to predict the direct bandgap of potential perovskite materials for this study. RF yielded the best experimental outcomes according to the following metrics: F1-score, Recall, and Precision, attaining scores of 86%, 85%, and 86%, respectively. This result demonstrates that ML has great potential in accelerating organic perovskites material discovery.

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Machine-learning-assisted discovery of perovskite materials with high dielectric breakdown strength

Abstract: In this paper, we have built a stepwise model based on the XGBoost machine learning algorithm to screen perovskite materials with high dielectric breakdown strength by comparing six machine learning...

Cited by 8 publications

References 48 publications

A Machine Learning Aided Yield Prediction Model for the Preparation of Cellulose Nanocrystals

A Machine Learning Aided Yield Prediction Model for the Preparation of Cellulose Nanocrystals

Machine learning in energy chemistry: introduction, challenges and perspectives

Exploring Data Augmentation and Dimension Reduction Opportunities for Predicting the Bandgap of Inorganic Perovskite through Anion Site Optimization

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