Machine learning aided design of perovskite oxide materials for photocatalytic water splitting

Tao, Qiuling; Lu, Tian; Ye, Sheng; Li, Long; Lu, Wei; Li, Minjie

doi:10.1016/j.jechem.2021.01.035

Cited by 85 publications

(47 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Perovskites, some of the most promising functional materials, have been widely applied in many fields, such as photovoltaics, ferroelectrics, and electrocatalysts. − To explore perovskite compounds that meet specific application requirements, various new types of perovskites have emerged. Among them, hybrid organic–inorganic perovskites (HOIPs) have attracted tremendous attention due to the advantages of inorganic and organic materials. − In its simplest form, the cubic crystal structure is composed of a three-dimensional framework of corner-sharing BX 6 octahedra, where the B site is a divalent cation and the X site is a halogen element (X = F, Cl, Br, or I), while A-site organic cation lies in a void surrounded by the octahedron.…”

mentioning

confidence: 99%

Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy

Zhang

et al. 2021

J. Phys. Chem. Lett.

Self Cite

View full text Add to dashboard Cite

Predicting the formability of perovskite structure for hybrid organic−inorganic perovskites (HOIPs) is a prominent challenge in the search for the required materials from a huge search space. Here, we propose an interpretable strategy combining machine learning with a shapley additive explanations (SHAP) approach to accelerate the discovery of potential HOIPs. According to the prediction of the best classification model, top-198 nontoxic candidates with a probability of formability (P f ) of >0.99 are screened from 18560 virtual samples. The SHAP analysis reveals that the radius and lattice constant of the B site (r B and LC B ) are positively related to formability, while the ionic radius of the A site (r A ), the tolerant factor (t), and the first ionization energy of the B site (I 1B ) have negative relations. The significant finding is that stricter ranges of t (0.84−1.12) and improved tolerant factor τ (critical value of 6.20) do exist for HOIPs, which are different from inorganic perovskites, providing a simple and fast assessment in the design of materials with an HOIP structure.

show abstract

mentioning

confidence: 99%

Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy

Zhang

et al. 2021

J. Phys. Chem. Lett.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Lu et al [93] provided two BODIPY dye models to predict the PCEs at http://materials-data-mining.com/bodipy/ Tao et al [198] offered one model to predict the bandgaps of perovskite oxides at http://materials-data-mining.com/ocpmdm/material_api/ahfga3d9puqlknig and another model to predict corresponding hydrogen production at http://materials-data-mining.com/ocpmdm/material_api/i0ucuyn3wsd14940…”

Section: High-throughput Screeningmentioning

confidence: 99%

“…For example, two boron-dipyrromethene (BOD-IPY) dye models were provided at http://materials-data-mining.com/bodipy/, which are widely accessible to use as established models for predicting the PCE values of BODIPY devices [93] . Tao et al constructed two models for predicting the bandgap (http://materials-data-mining.com/ocpmdm/material_api/ahfga3d9puqlknig) and hydrogen production rate (http://materials-data-mining.com/ocpmdm/material_api/i0ucuyn3wsd14940) of perovskite oxides [198] . It is only required for the users to provide chemical formulas to predict the bandgap and formulas plus experimental conditions to predict the hydrogen production rate.…”

Section: Online ML Modelsmentioning

confidence: 99%

Recent progress in the data-driven discovery of novel photovoltaic materials

Liu¹,

Li²,

Lü³

et al. 2022

J Mater Inf

Self Cite

View full text Add to dashboard Cite

The discovery of new photovoltaic materials can facilitate technological progress in clean energy and hence benefit overall societal development. Machine learning (ML) and deep learning (DL) technologies integrated with domain knowledge are revolutionizing the traditional trial-and-error research paradigm that is associated with high costs, inefficiency, and significant human effort. This review provides an overview of the recent progress in the data-driven discovery of novel photovoltaic materials for perovskite, dye-sensitized and organic solar cells. The integral workflow of the ML/DL training progress is briefly introduced, covering data preparation, feature engineering, model building and their applications. The cutting-edge challenges and issues in the ML/DL workflow are summarized specifically for photovoltaic materials. Real examples are emphasized to illustrate how to utilize ML/DL techniques in the discovery of novel photovoltaic materials. The prospects and future directions of the data-driven discovery of novel photovoltaic materials are also provided.

show abstract

“…The authors insisted that Materials 4.0 could use data to overcome human limitations, such as trial and error, and one of the methods of Materials 4.0 is machine learning (ML). Several materials studies using ML have been conducted successfully 10–13 . Kazi et al developed an artificial neural network (ANN) model for fiber‐reinforced polymeric composites to satisfy required target properties while achieving the target filler content of composites 10 .…”

Section: Introductionmentioning

confidence: 99%

“…To solve this problem, they proposed a prediction model with a drug molecular structure and protein sequence, and the proposed model proved superior to comparative methods. Tao et al attempted to accelerate the discovery of efficient perovskite photocatalysts for photocatalytic water splitting using ML 13 . The authors used the Pearson correlation coefficient to compare the performances of different algorithms, including gradient boosting, support vector machine, ANN, and random forest (RF).…”

Section: Introductionmentioning

confidence: 99%

Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters

Joo

Park

Lim

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

The physical properties required in polypropylene composites (PPCs) vary depending on the purpose of use. In the manufacturing of PPCs, it is crucial to determine the types and quantities of numerous reinforcements to meet the required physical properties. Owing to industrial complexity, most PPC manufacturers produce the composites repeatedly until the desired physical properties are obtained. Hence, to reduce trial and error, we developed prediction models for the physical properties of PPCs based on commercial recipe data. The recipe data included information about five physical properties of composites manufactured using 90 materials. In complex industrial environments, because one recipe is usually composed of 2–12 materials, numerous combinations of data sets are created. It causes the lack of the same material combination data sets and thus makes it difficult to develop a good performance model. Therefore, a novel categorization process is suggested as data preprocessing to overcome the data imbalance problem. The models for predicting the five physical properties (flexural strength, melting index, tensile strength, specific gravity, and flexural modulus) were developed using random forest, and the performance of the prediction models was improved via hyperparameter optimization. Furthermore, the effects of the materials on the performance of the models were numerically described through variable importance analysis. Finally, a software was developed to implement the prediction models in the industry. The software was applied to a commercial composite and achieved high accuracy, demonstrating the effectiveness of this study. Thus, the software suggests decision‐making solutions to save cost and time by reducing the trial and error in the industrial environment with high complexity.

show abstract

Machine learning aided design of perovskite oxide materials for photocatalytic water splitting

Cited by 85 publications

References 74 publications

Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy

Predicting the Formability of Hybrid Organic–Inorganic Perovskites via an Interpretable Machine Learning Strategy

Recent progress in the data-driven discovery of novel photovoltaic materials

Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters

Contact Info

Product

Resources

About