Using Data Mining To Search for Perovskite Materials with Higher Specific Surface Area

The specific surface area (SSA) of ABO3-type perovskite is one of the important properties associated with photocatalytic ability. In this work, data mining methods were used to explore the relationship between the SSA (in the range of 1–60 m2 g–1) of perovskite and its features, including chemical compositions and technical parameters. The genetic algorithm–support vector regression method was used to screen the main features for modeling. The correlation coefficient (R) between the predicted and experimental SSAs reached as high as 0.986 for the training data set and 0.935 for leave-one-out cross-validation. ABO3-type perovskites with higher SSA can be screened out using the Online Computation Platform for Materials Data Mining (OCPMDM) developed in our laboratory. Further, an online web server has been developed to share the model for the prediction of SSA of ABO3-type perovskite, which is accessible at http://118.25.4.79/material_api/csk856q0fulhhhwv

Inverse Design of Hybrid Organic–Inorganic Perovskites with Suitable Bandgaps via Proactive Searching Progress

Hybrid organic–inorganic perovskites (HOIPs) have shown the encouraging development in solar cells that have achieved excellent device performance. One of the most important issues has been focused on finding Pb-free candidates with suitable bandgaps, which could accelerate the commercialization of environmentally friendly HOIP-based cells. Herein, we propose a new inverse design method, proactive searching progress (PSP), to efficiently discover potential HOIPs from universal chemical space by combining machine learning (ML) techniques. Compared to the pioneering work on this topic, we carried out our ML study based on 1201 collected HOIP samples with experimental bandgaps rather than theoretical properties. On the basis of 25 selected features, a weighted voting regressor ML model was constructed to predict bandgaps of HOIPs. The model comprehensively embedded four submodels and performed the coefficient determinations of 0.95 for leaving-one-out cross-validation and 0.91 for testing set. The feature analysis revealed that the tolerance factor (tf) below 0.971 and the new tolerance factor (τf) in 3.75–4.09 contributed to lower bandgaps and vice versa. By applying the PSP method, the Pb-free HOIPs with optimal bandgaps were successfully designed from a generated chemical space comprising over 8.20 × 1018 combinations, which included 733848 candidates (e.g., Cs0.334FA0.266MA0.400Sn0.769Ge0.003Pd0.228Br0.164I2.836) with an optimal bandgap of 1.34 eV for single junction solar cells, 1511073 large-bandgap candidates (e.g., Cs0.392FA0.016MA0.592Cr0.383Sr0.347Sn0.270Br1.171I1.829) for top parts in tandem solar cells (TSCs), and 20242 low-bandgap ones (e.g., MA0.815FA0.185Sn0.927Ge0.073I3) for bottom cells in TSCs. Finally, three new HOIPs were synthesized with an average bandgap error 0.07 eV between predictions and experiments. We are convinced that the proposed PSP method and ML progress could facilitate the discovery of new promising HOIPs for photovoltaic devices with the desired properties

Predicting Experimental Formability of Hybrid Organic–Inorganic Perovskites via Imbalanced Learning

Hybrid organic–inorganic perovskites (HOIPs) have gained lots of attention in the photovoltaic field, but their further development is restrained by contaminant and stability. More potential HOIPs should be explored for photovoltaic devices. In this work, we collected 539 HOIPs and 24 non-HOIPs experimentally synthesized to explore novel compositions of HOIPs. An imbalanced learning was carried out, and the best classification model achieved a leaving-one-out cross-validation accuracy of 100.0% and a test accuracy of 96.1%. The A site atomic radii (ARA), A site ionic radius (IRA), and tolerance factor (tf) were identified as the most important features. ARA IRA tf < 1.01 contributed to perovskite formability, and the formability possibilities of the corresponding samples were over 90.0%. Potential A site organic fragments were identified for perovskite solar cells, such as dimethylamine, hydroxylamine, hydrazine, etc. Finally, three new Sn–Ge mixed systems of HOIPs were successfully synthesized, which was consistent with the model predictions

Predicting Experimental Formability of Hybrid Organic–Inorganic Perovskites via Imbalanced Learning

Hybrid organic–inorganic perovskites (HOIPs) have gained lots of attention in the photovoltaic field, but their further development is restrained by contaminant and stability. More potential HOIPs should be explored for photovoltaic devices. In this work, we collected 539 HOIPs and 24 non-HOIPs experimentally synthesized to explore novel compositions of HOIPs. An imbalanced learning was carried out, and the best classification model achieved a leaving-one-out cross-validation accuracy of 100.0% and a test accuracy of 96.1%. The A site atomic radii (ARA), A site ionic radius (IRA), and tolerance factor (tf) were identified as the most important features. ARA IRA tf < 1.01 contributed to perovskite formability, and the formability possibilities of the corresponding samples were over 90.0%. Potential A site organic fragments were identified for perovskite solar cells, such as dimethylamine, hydroxylamine, hydrazine, etc. Finally, three new Sn–Ge mixed systems of HOIPs were successfully synthesized, which was consistent with the model predictions

Machine Learning Combined with Weighted Voting Regression and Proactive Searching Progress to Discover ABO_3‑δ Perovskites with High Oxide Ionic Conductivity

ABO3‑δ-type perovskites are one of the important oxygen ion conductors because of the enhanced properties through adjustments to the composition via elemental doping. In this work, machine learning combined with weighted voting regression (WVR) and proactive searching progress (PSP) was used to develop a model with high accuracy for the prediction of the oxide ionic conductivity of doped ABO3‑δ perovskites. After feature selection, algorithm selection, and parameter optimization, Gradient Boosting regression (GBR), random forest regression (RFR), and extra trees regression (ETR) were determined to be the optimal methods for WVR in constructing the integrated model. The R values of leave-one-out cross-validation (LOOCV) and the test set for the integrated model MWVR could reach 0.812 and 0.920, respectively. After the PSP was conducted, a total of 179 perovskites with high oxide ionic conductivity were discovered. PSP searching identified 8 types of perovskites with high oxide ionic conductivity. Pattern recognition was employed to identify the optimization area that exhibited a high oxide ionic conductivity. Visualization of factor effects was used to visualize the effect of the doping element type and ratio on the oxide ionic conductivity. The Shapley Additive exPlanations (SHAP) analysis of the significant features revealed that Ra/Rb had the highest influence on the oxide ionic conductivity with a negative impact. The developed integrated model, explored patterns, and optimization areas in this work can serve as a valuable guide for the discovery and design of perovskites with high oxide ionic conductivity

Predicting Experimental Formability of Hybrid Organic–Inorganic Perovskites via Imbalanced Learning

Hybrid organic–inorganic perovskites (HOIPs) have gained lots of attention in the photovoltaic field, but their further development is restrained by contaminant and stability. More potential HOIPs should be explored for photovoltaic devices. In this work, we collected 539 HOIPs and 24 non-HOIPs experimentally synthesized to explore novel compositions of HOIPs. An imbalanced learning was carried out, and the best classification model achieved a leaving-one-out cross-validation accuracy of 100.0% and a test accuracy of 96.1%. The A site atomic radii (ARA), A site ionic radius (IRA), and tolerance factor (tf) were identified as the most important features. ARA IRA tf < 1.01 contributed to perovskite formability, and the formability possibilities of the corresponding samples were over 90.0%. Potential A site organic fragments were identified for perovskite solar cells, such as dimethylamine, hydroxylamine, hydrazine, etc. Finally, three new Sn–Ge mixed systems of HOIPs were successfully synthesized, which was consistent with the model predictions

Search for ABO₃ Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies

Ferroelectric perovskites are one of the most promising functional materials due to the pyroelectric and piezoelectric effect. In the practical applications of ferroelectric perovskites, it is often necessary to meet the requirements of multiple properties. In this work, a multiproperties machine learning strategy was proposed to accelerate the discovery and design of new ferroelectric ABO3-type perovskites. First, a classification model was constructed with data collected from publications to distinguish ferroelectric and nonferroelectric perovskites. The classification accuracies of LOOCV and the test set are 87.29% and 86.21%, respectively. Then, two machine learning strategies, Machine-Learning Workflow and SISSO, were used to construct the regression models to predict the specific surface area (SSA), band gap (Eg), Curie temperature (Tc), and dielectric loss (tan δ) of ABO3-type perovskites. The correlation coefficients of LOOCV in the optimal models for SSA, Eg, and Tc are 0.935, 0.891, and 0.971, respectively, while the correlation coefficient of the predicted and experimental values of the SISSO model for tan δ prediction could reach 0.913. On the basis of the models, 20 ABO3 ferroelectric perovskites with three different application prospects were screened out with the required properties, which could be explained by the patterns between the important descriptors and the properties by using SHAP. Furthermore, the constructed models were developed into web servers for the researchers to accelerate the rational design and discovery of ABO3 ferroelectric perovskites with desired multiple properties

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization

High-entropy alloys (HEAs) with high hardness and high ductility can be considered as candidates for wear-resistant applications. However, designing novel HEAs with multiple desired properties using traditional alloy design methods remains challenging due to the enormous composition space. In this work, we proposed a machine-learning-based framework to design HEAs with high Vickers hardness (H) and high compressive fracture strain (D). Initially, we constructed data sets containing 172,467 data with 161 features for D and H, respectively. Four-step feature selection was performed, with the selection of 12 and 8 features for the D and H prediction models based on the optimal algorithms of the support vector machine (SVR) and light gradient boosting machine (LightGBM), respectively. The R2 of the well-trained models reached 0.76 and 0.90 for the 10-fold cross validation. Nondominated sorting genetic algorithm version II (NSGA-II) and virtual screening were employed to search for the optimal alloying compositions, and four recommended candidates were synthesized to validate our methods. Notably, the D of three candidates have shown significant improvements compared to the samples with similar H in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. Analyzing the candidates, we have recommended suitable atomic percentage ranges for elements such as Al (2–14.8 at %), Nb (4–25 at %), and Mo (3–9.9 at %) in order to design HEAs with high hardness and ductility

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization

High-entropy alloys (HEAs) with high hardness and high ductility can be considered as candidates for wear-resistant applications. However, designing novel HEAs with multiple desired properties using traditional alloy design methods remains challenging due to the enormous composition space. In this work, we proposed a machine-learning-based framework to design HEAs with high Vickers hardness (H) and high compressive fracture strain (D). Initially, we constructed data sets containing 172,467 data with 161 features for D and H, respectively. Four-step feature selection was performed, with the selection of 12 and 8 features for the D and H prediction models based on the optimal algorithms of the support vector machine (SVR) and light gradient boosting machine (LightGBM), respectively. The R2 of the well-trained models reached 0.76 and 0.90 for the 10-fold cross validation. Nondominated sorting genetic algorithm version II (NSGA-II) and virtual screening were employed to search for the optimal alloying compositions, and four recommended candidates were synthesized to validate our methods. Notably, the D of three candidates have shown significant improvements compared to the samples with similar H in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. Analyzing the candidates, we have recommended suitable atomic percentage ranges for elements such as Al (2–14.8 at %), Nb (4–25 at %), and Mo (3–9.9 at %) in order to design HEAs with high hardness and ductility

Accelerated Design for High-Entropy Alloys Based on Machine Learning and Multiobjective Optimization

High-entropy alloys (HEAs) with high hardness and high ductility can be considered as candidates for wear-resistant applications. However, designing novel HEAs with multiple desired properties using traditional alloy design methods remains challenging due to the enormous composition space. In this work, we proposed a machine-learning-based framework to design HEAs with high Vickers hardness (H) and high compressive fracture strain (D). Initially, we constructed data sets containing 172,467 data with 161 features for D and H, respectively. Four-step feature selection was performed, with the selection of 12 and 8 features for the D and H prediction models based on the optimal algorithms of the support vector machine (SVR) and light gradient boosting machine (LightGBM), respectively. The R2 of the well-trained models reached 0.76 and 0.90 for the 10-fold cross validation. Nondominated sorting genetic algorithm version II (NSGA-II) and virtual screening were employed to search for the optimal alloying compositions, and four recommended candidates were synthesized to validate our methods. Notably, the D of three candidates have shown significant improvements compared to the samples with similar H in the original data sets, with increases of 135.8, 282.4, and 194.1% respectively. Analyzing the candidates, we have recommended suitable atomic percentage ranges for elements such as Al (2–14.8 at %), Nb (4–25 at %), and Mo (3–9.9 at %) in order to design HEAs with high hardness and ductility