Introduction

The amazing properties of superconducting materials are a direct consequence of quantum mechanics that emerge on a large scale1. The two basic characteristics of superconductors that make this class of materials different from others include: a) offering no resistance to the flow of electric currents, and b) complete exclusion of magnetic field2. No comprehensive theory capable of predicting the transition temperatures (Tc) of superconducting materials has yet been presented to date and the discovery of new superconductors still relies on expert intuition and is largely dependent on trial and error based on experience3. Hence, empirical laws have for many years served as guides for researchers in their efforts to fabricate new superconducting materials4.

Condensed Matter Physics strives to discover the interactions of materials at the atomic level since material properties are derived from these interactions5. Prediction and determination of the microscopic properties of materials presuppose the solution of the Schrodinger equation for a Many-Body system. However, solving this equation for such systems is practically impossible due to the vast Hilbert space needed to handle them, especially for highly correlated materials. Consequently, a solution adopted in most cases is to employ approximate methods6,7,8. One of these methods is Density Functional Theory (DFT) which is based on the Hohenberg–Kohn and Kohn–Sham theorems and has a substantial record of success in predicting material properties and solving the associated quantum mechanics problems9,10,11. Despite its outstanding achievements, the theory has some limitations in its current form; for instance, it employs approximation for exchange–correlation functional, yields errors when used for strong correlation systems, can only be employed for a small number of atoms, and is hampered by increasing computational costs and runtime with increasing system size10,12,13,14,15. Strong electron–electron correlations in superconducting materials make it extremely challenging to perform first-principles calculations to determine their structural properties and predict their Tc3,9, making searching for novel alternative approaches inevitable.

As alternative strategies for solving quantum mechanics problems, machine learning methods offer lower computation costs, shorter execution times, accurate predictions, and faster development cycles9,12,13. Being data-driven and given the fact that huge amounts of data have been produced over the years, machine learning methods encourage researchers to utilize them for discovering novel materials and predicting their properties4,5,6,16. Materials Science is nowadays said to have entered its fourth stage of evolution, termed “Data-Based Materials Science”, a term borrowed from Thomas Samuel Kuhn to describe the field’s development6,12,17,18. Figure 1 illustrates the four (empirical, theoretical, computational, and data-driven) paradigms of materials science. To date, large amounts of theoretical and experimental data have been collected in the three traditional (i.e., empirical, theoretical, and computational) paradigms; the next step, logically, is to apply the new innovative tools developed by artificial intelligence, which are capable of extracting knowledge from such data6,12,18,19,20,21,22.

Figure 1
figure 1

Four paradigms of materials science from the beginning to the current.

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Given the importance of the Tc values of superconducting materials, researchers have in recent years developed machine learning-based models for predicting this quantity. Selecting 21,263 superconducting materials and utilizing 80 atomic descriptors for each compound, Hamidieh4 used the XGBoost algorithm to design a model for predicting of Tc. Stanev et al.16 employed the Random Forest algorithm to develop a model using 132 atomic features of Magpie descriptors for 6196 superconducting compounds. Konno et al.3 implemented a convolutional neural network (CNN) model (i.e., a deep learning model) to predict the Tc values of about 13,000 superconducting materials. They represented their materials using an innovative “periodic table reading” method. The dimensions of the representation were 4 × 32 × 7, with 4 representing the four orbitals of s, p, d, and f corresponding to the valence electrons of each element in a compound, and 32 and 7 denoting the dimensions of the periodic table. Dan et al.23 developed the ConvGBDT model by merging the convolutional neural network (CNN) and the gradient boosting decision tree (GBDT) models. For the three datasets of DataS, DataH, and DataK, the authors used the Magpie descriptors to represent materials and the ConvGBDT model to predict Tc values. Li et al.11 introduced a hybrid neural network (HNN) model as a combination of a convolutional neural network (CNN) and a long short-term memory neural network (LSTM). They utilized atomic vectors and employed both the one-hot and Magpie material characterization methods to represent superconductors in the feature space. The authors found that the Magpie features generally outperformed the one-hot features. Roter et al.24 employed the Bagged Tree method (a variant of the Random Forest algorithm) to design a model for predicting Tc. They represented superconducting materials using a chemical composition matrix as the feature space. The matrix had about 30,000 rows and 96 columns, wherein each row corresponded to a chemical formula, and the columns contained the 96 primary elements of the periodic table. Each entry in this matrix was filled with an index corresponding to the elements of each chemical compound. Quinn et al.25 utilized a Crystal Graph Convolutional Neural Network (CGCNN) model to integrate classification and regression models within a pipeline to identify candidates of high-temperature superconductors from among the 130,000 compounds in the Materials Project. In the crystal-graph representation of materials, the connections between atoms represent the graph’s edges, and the locations of the atoms and their properties represent the vertices.

The main objective of the current research is to design a suitable and reliable model for predicting the Tc values of superconducting materials using machine learning approaches. While the algorithm and the dataset are the two indispensable research tools in data science, the present study attaches more importance to the dataset than the algorithm. After carefully cleaning data, we generate a suitable feature space for superconducting materials. The main advantages of the present work over previous ones include: (1) Establishing more appropriate feature space related to superconducting Tc and (2) Identifying the features most related to the Tc values of superconducting materials. We reach significant results by designing the Jabir package to produce 322 atomic features for each compound and Soraya package for selecting features.

Data

Data set

Two essential steps must be taken before statistical learning can predict Tc in superconducting materials. The first involves collecting and preprocessing a dataset, and the second is adopting a suitable algorithm for the learning process and model development on that dataset. According to Halevy et al.26, the first step is of greater significance as data scientists typically devote about 80% of their efforts to datasets and their preprocessing27; the same is valid with the present work using SuperCon dataset (https://doi.org/10.48505/nims.3739), currently the largest and most comprehensive superconducting materials database containing 33,407 superconducting compounds.

Here, a significant contribution is done by executing distinct steps of data pre-processing and providing detailed explanations for each step. Ultimately, following the implementation of various data pre-processing phases and the exclusion of problematic data, the DataG dataset consisting of 13,022 superconducting compounds is derived.

Cleaning the dataset

Dealing with missing and duplicated data

The SuperCon dataset lacks the transition temperature values for 7088 compounds. These cases are identified as missing data and removed from the dataset. Along with that, we remove 7418 data duplications. Among these, 1264 compounds are regarded as duplicates due to the displacement of data elements; examples include: MgB2, B2Mg, Ag7B1F4O8, Ag7F4O8B1, Al0.1Si0.9V3, V3Si0.9Al0.1, Zr2Co1, Co1Z2, ….

Dealing with problematic data

(1) We eliminate 5348 compounds whose element subscripts are X, Y, Z, D, x, y, z, and d. (2) We remove problematic compounds such as: HgSr2Ho0.333Ce0.667Cu2O6=z, Ba2Cu1.2Co2.4O2,4, Ag7Bf4O8, Hg0.3Pb0.7Sr1.75La0.25CuO4+2, Ho0.8Ca0.2Sr2Cu2.8P0.2Oz+0.8, Bi1.6Pb0.4Sr2Ca2Cu3F0.8Oz-0.8. (3) Compounds containing the elements not included in the periodic table are ignored. (4) Given the objective of predicting transition temperatures for superconductors at ambient pressure, those created under non-ambient pressures (e.g., La1H10, H2S1, H3S1, D3S1, …) are removed from the dataset. (5) The compound YBa2CuO6050 is eliminated on the grounds that the oxygen subscript of 6050 might be incorrect4. (6) We dismissed 70 compounds whose transition temperatures are reported to be zero. (7) Finally, the compound Pb2CAg2O6 is discarded due to the unreasonable transition temperature of 323 K reported for this compound.

Data correction

(1) According to the SuperCon reference28, the transition temperature of the iron-based superconductor CsEuFe4As4 is nearly 30 K, while the SuperCon dataset records it as 287 K. Therefore, it is modified to 28.7 K. Moreover, the compound Sm1Ba-1Cu3O6.94 is substituted with Sm1Ba1Cu3O6.94. (2) Bi1.6Pb0.4Sr2Cu3Ca2O1013 is altered to Bi1.6Pb0.4Sr2Cu3Ca2O10.13 because the nearby data rows containing formulas with O10.xx4.

Dealing with multiple temperatures reported for a single compound

One limitation in the SuperCon dataset is the presence of multiple Tc values reported for 2132 compounds, posing a challenge for accurate analysis. For instance, MgB2 alone has been reported to exhibit 47 different transition temperatures ranging from 5 to 40.5 K. To tackle this challenge, it has been recommended to consider average transition temperatures for compounds that have multiple Tc values reported in the dataset. Prior to determining the average Tc value, it is essential to exclude compounds whose reported transition temperatures display significant dispersion. To achieve this, the standard deviation of the different transition temperatures for each compound is calculated and compounds with standard deviations greater than 20 K are removed from the dataset. Performing this procedure leads to the elimination of 18 compounds.

Detecting outliers

Undoubtedly, outliers in a dataset can pose problems in identifying underlying patterns, resulting in diminishing system performance and accuracy29. In this study, the outlier data are detected using the Z-score method30 and the PyOD package31, both renowned tools in the field of anomaly detection. After a meticulous examination, the outliers are identified and excluded according to the three following distinct aspects:

  1. (1)

    Transition temperature: The average transition temperature of remaining compounds ranges from 0.0005 to 250 K. Using the abovementioned techniques, 10 superconducting materials with average transition temperatures outside the 0.01–136 K range are identified as outliers and removed. Figure 2 illustrates the Tc distribution of the few superconducting material families.

  2. (2)

    Number of elements: Fig. 3 shows the number of compounds according to the number of constituent elements. A subset of compounds with one, eight, and ten elements are identified as outliers and subsequently removed, resulting in the elimination of 81 superconducting compounds.

  3. (3)

    The summation of subscripts: Implementing the abovementioned techniques reveals that six compounds exhibited subscript summations exceeding 100 that are subsequently removed as outliers.

Figure 2
figure 2

Distribution of transition temperature of superconducting materials.

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Figure 3
figure 3

The number of superconducting compounds based on the number of their constituent elements.

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Our meticulous data-cleaning procedures yield a refined dataset, called DataG dataset, containing 13,022 compounds.

Computational methods

Machine learning algorithm

In this study, we use the CatBoost algorithm as a machine learning ensemble technique based on Gradient Boosted Decision Trees (GBDT) proposed by Yandex Company. GBDT is an efficient tool for solving regression and classification issues in big data sets. CatBoost is a Decision Tree based algorithm and open-source implementation for supervised machine learning that involves two innovations: Ordered Target Statistics and Ordered Boosting. Researchers have successfully employed CatBoost for machine learning investigations incorporating Big Data since its launch in late 2018. Numerous applications have been reported for CatBoost in various fields, including astronomy, finance, medicine, biology, electrical utilities fraud, meteorology, psychology, traffic engineering, cyber-security, biochemistry, and marketing32. However, the application of CatBoost has not yet been reported for predicting superconducting transition temperatures. This study uses the algorithm to find if it can efficiently identify relationships and patterns between features and Tc. We show that through the creation of atomic features for superconducting material, CatBoost algorithm provides a model with very good accuracy.

Generating the feature space

After preprocessing the data set, we must extract atomic features in a “data representation” procedure. There are two main approaches for representing compounds: The first is based on chemical formulas, and the second on crystal structure23. The atomic features are generated for superconducting materials using the first approach for our purposes.

In fact, machine learning algorithms recognize a compound by its characteristics, i.e. the identifier and characteristic of a compound are the features that are consider for the compound. This process is called data representation. Figure 4 shows how to calculate atomic features.

Figure 4
figure 4

Atomic feature generation workflow for a compound.

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We design and develop the Python language package called Jabir to generate 322 atomic features for each compound of all types, including superconducting materials. The package calculates eight statistical relationships (e.g., variance and mean) for each physical feature (e.g., magnetic moment) based on the three components of Element, Subscript, and Fraction. Figure 5 depicts the workflow of the feature-generating by Jabir.

Figure 5
figure 5

Jabir package generates 322 atomic features for each compound, including eight statistical relations for 12 physical features based on the three components of Elements, Subscript, and Fraction. Also, for the four Ionic Radius, Vander Waals Radius, Period Number and Group Number features, only the Element component is calculated because the two others, subscript-based and fraction-based ones, are meaningless for these features. Therefore, 320 features have been generated, and finally, 2 more features are added to them: the number of constituent elements of each compound and the sum of their subscripts. In short, there are 322 atomic features for each compound (12 × 3 × 8 + 4 × 1 × 8 + 2 = 322).

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For illustration, consider the compound Mg0.9Fe0.1B2 composed of three elements. The subscripts are 0.9, 0.1 and 2, while the fractions of the elements in the compound, obtained from Eq. 1, are 0.3, 0.033, and 0.666, respectively.

$$Fraction = \frac{Subscript}{{\sum Subscript}}$$
(1)

As mentioned, the atomic features are generated based on the three components of Element, Subscript, and Fraction. The fraction-based atomic features are multiplied by the fraction of the element in the compound.

Similarly, the subscript-based atomic features are multiplied by the subscript of that element in the compound. However, the atomic features based on element (Element-based) are based solely on elemental values; in other words, the elemental value is multiplied by one, ignoring the related fraction or subscript. It should also be noted that the Jabir package solely calculates the element-based atomic features for the four Ionic Radius, Vander Waals Radius, Period Number and Group Number features because the two subscript-based and fraction-based ones are meaningless for these features. Table 1 briefly explains the process used for calculating the mean thermal conductivity of the Mg0.9Fe0.1B2 compound, for instance. To learn more about Jabir's features, see the Supplementary Information; we have explained briefly all the 30 most significant features depicted in Fig. 7.

Table 1 Calculation of the mean thermal conductivity for Mg0.9Fe0.1B2 compound.
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At first glance, it seems fraction-based and subscript-based are the same thing, and we should choose one. However, the fraction of some elements in some compounds consisting of the same elements can be equal while they have different subscripts. The subscript-based must also be considered in atomic features space to account for this difference. For instance, the compounds Y1Fe2Si2 and Y2Fe3Si5 have identical fractions of “Y” (namely, 0.2), while it is rational to think that this same element has different effects in these two compounds. Clearly, among the three types of atomic features, only the subscript-based one accounts for this difference, demonstrating the reason why the subscript-based feature must be used.

Feature selection

Feature selection methods are utilized to determine the best feature subset. Some advantages of feature selection include reduced overfitting, improved accuracy, reduced training time, simplified model design, faster convergence, enhanced generalization, and improved robustness to noise33,34,35,36. Feature selection methods help pick out the subset of attributes most relevant to the Tc of superconducting materials. Generally, for a feature space of N features, there are 2N subsets of features. For example, the current feature space with 322 features leads us to select a subset among 2322 ≈ 8.54 × 1096 feature subsets. Due to the enormous number of subsets, we need sophisticated methods to overcome computational costs. There are generally four general methods for selecting a subset of features: filter, wrapper, embedded, and hybrid33,35,37. In this study, the various feature selection techniques are tested carefully and evaluated for their efficiency and effectiveness against such evaluation criteria as the coefficient of determination (R2), Eq. 2, and root mean square error (RMSE), Eq. 323. Finally, we developed a novel and innovative hybrid method. This method has been published in the form of a Python package called Soraya.

$$R^{2} = 1 - \frac{{\mathop \sum \nolimits_{i = 1}^{n} \left( {y_{i} - \hat{y}_{i} } \right)^{2} }}{{\mathop \sum \nolimits_{i = 1}^{n} \left( {y_{i} - \overline{y}} \right)^{2} }}$$
(2)
$$RMSE = \sqrt {\frac{1}{n}\mathop \sum \limits_{i = 1}^{n} \left( {y_{i} - \hat{y}_{i} } \right)^{2} }$$
(3)

Using the proposed feature selection technique, 30 of the most important features generated by the Jabir package for DataG are selected. The results of these comparisons are reported in Table 2.

Table 2 Evaluation and comparison of various feature selection techniques (bold: in this study).
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The proposed innovative hybrid method for selecting the best subset of feature space calculates the two-by-two correlation of all 322 features in its first step. In the second step, all the features with absolute Pearson correlation criterion values greater than 0.80 are grouped into distinct clusters. The Pearson correlation criterion is a parametric statistical method that allows to determine the existence or absence of a linear relationship between two quantitative variables38. This step categorizes 304 features into 62 clusters and retains 18 features with correlations less than 0.80 for the following steps. (Each cluster contains a varying number of features. For example, one cluster may consist of only two correlated features, while another may comprise five. Therefore, the rationale behind Soraya's decision to group 304 features into 62 clusters is based on the characteristics of the features presented in the dataset.). This step aims to retain the most important features from each cluster and eliminate the remainder as redundant. Features with a correlation greater than 0.8 mean that they have very strong correlation39. The objective of feature selection is to identify a subset of the original features from a provided dataset by eliminating irrelevant and redundant features40. Furthermore, as the feature space dimensions decrease, the learning model's accuracy will increase41. We should keep only one of those features which they have very strong correlation and remove the others, because they are redundant features that do not provide any new information42.

In the third step, the learning process of the model is performed independently for each cluster, with the most significant feature in each cluster being selected and the others eliminated. As a result of grouping the features into 62 distinct clusters, 62 features remain in this step. Once the duplicate features have been deleted, 55 features remain.

In the fourth step, 18 features are added to the 55 features obtained above. Subsequently, the SHAP (SHapley Additive exPlanations) method, which is based on the game theory for explaining the output of machine learning models43,44, is employed to sort the 73 features according to the significance level. In this step, the SHAP method acts as a filter method.

In the fifth step, the 5 most significant features, as identified and sorted by the SHAP method in the previous step, are initially selected. Using the forward selection (wrapper method), the remaining 68 (i.e., 18 + 55 − 5) features are added one by one to the 5 features, until 30 features are selected from among the most significant ones. (The 30 most significant features give the highest accuracy for the model; The Soraya package is designed in such a way that it shows the amount of accuracy with the addition of each feature.) The steps outlined above are depicted in Fig. 6.

Figure 6
figure 6

Workflow related to the innovative hybrid method (Soraya Package). Through a 5-step process, this technique can efficiently identify the most significant features in the dataset while eliminating any redundant features.

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The DataG dataset, which contains 13,022 superconducting materials, comprises 83 elements from the periodic table. As a result, 83 columns are created in which the fractions of the constituent elements for each compound are recorded. This process is illustrated in Table 3. Finally, we add this feature vector to the previously selected features to make a final feature space with 113 (83 + 30) dimensions.

Table 3 Ba0.8Fe2Se2 is represented by an array of 83 columns, with each column containing the fraction of constituent elements present in Ba0.8Fe2Se2.
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Results

Identifying key features

During the feature selection process in Section "Feature Selection", we employed an innovative hybrid technique called “Soraya package” to pick 30 of the most significant features. Subsequently, in Fig. 7, we sorted these selected features using the capability of the CatBoost algorithm. In the Supplementary Information, we have explained those features depicted in Fig. 7.

Figure 7
figure 7

Using Soraya package, 30 of the most significant features were selected from among 322 features, and subsequently, the CatBoost algorithm was employed to sort these selected 30 features.

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Across various studies4,38,45, including the current study, researchers have discovered that the thermal conductivity stands out as the most important feature among different features in determining the Tc of superconducting materials. Theoretically, the thermal conductivity of superconductors provides significant clues about the nature of their charge carriers, phonons, and the scattering processes occurring between them46. Thermal conductivity refers to the ability of a material to conduct heat. The significance of thermal conductivity is directly connected to the concentration of particles capable of transferring heat45,47. The concentration of the superconducting particles (ns) is related to a characteristic length describing the superconducting state, namely, the London penetration depth (λ), \({\uplambda }^{2}=\frac{m}{{q}^{2}{n}_{s}{\mu }_{0}}\), where m, q, ns are mass, charge and concentration of superconducting particles respectively and μ0 is magnetic constant38,48. The transition temperature of a superconductor is associated with both the London penetration depth and the coherence length. In other words, the formation and destruction of the superconducting state is related to the London penetration depth and the coherence length38,45,48. On the other hand, the results of this study and other studies4,38,45 show that the superconducting transition temperature has a strong correlation with the thermal conductivity; Among the 322 features, the range of thermal conductivity has the strong correlation (0.68) with the Tc; see Fig. 7. Then, it could be concluded that the results of this research are consistent with the results of theoretical works.

Predicting the superconducting materials’ T c values

For the DataG dataset, which contained 13,022 superconducting materials, 90% of the dataset is allocated to the training dataset and 10% to the test one (i.e., 1303 compounds). Using the model created during training, the CatBoost algorithm predicted a Tc value for every 1303 compounds in the test dataset (Fig. 8). The R2 and RMSE evaluation criteria are 0.952 and 6.45, respectively, superior to those reported in the literature. Table 4 compares the values for the R2, RMSE, and MAE evaluation criteria obtained in the present study and those reported elsewhere. The model proposed here yields R2, RMSE, and MAE values superior to those previously reported.

Figure 8
figure 8

Comparison of predicted Tc of 1303 superconducting compounds by machine learning model according to experimental Tc.

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Table 4 Evaluation values for predicting the Tc of superconducting materials related to the current research (bold) and other works.
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Furthermore, the procedure employed for DataG (namely, creating new features, selecting features, tuning hyperparameters, etc.) is also applied to DataS, DataK, and DataH datasets, which led to improved evaluation criteria (Table 4).

The model thus developed is subsequently used to predict the Tc values for SmFeAsO0.8F0.2, SmFeAsO0.7F0.3 and three new Iron-based superconducting materials not included in the data set and for which no Tc values had yet been reported. As shown in Table 5, the Tc value of the main compound increases as the Fluorine element increases to an optimal doping content. This increase in Tc aligns with the experimental results. Table 5 also indicates that we can play around with elements (e.g., substitution, changing contribution) and find trends for increasing Tc for a specific compound, which can help material scientists to design high-temperature superconductors. It should be emphasized that none of the compounds mentioned in Table 5 are included in the SuperCon dataset.

Table 5 Prediction of Tc of three new Iron-based superconducting compounds using machine learning model.
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Moreover, the model was used to predict the Tc values for a few superconducting compounds not included in the DataG but for which Tc values had been previously reported in the literature. The results are provided in Table 6 for comparison. Clearly, a great agreement can be observed between the two Tc values obtained by experiments and by the machine learning method used in this study.

Table 6 The machine learning model developed in this study accurately predicts the Tc of several superconducting compounds, none of which are present in the original SuperCon dataset.
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Conclusion

In the realm of materials science, artificial intelligence stands as a powerful tool for predicting material properties. In this study, the CatBoost algorithm was employed to predict the Tc values of superconducting materials, marking a novel approach. For this purpose, data pre-processing of the SuperCon dataset was accomplished as a significant step in data science to develop a new dataset called DataG containing 13,022 superconducting compounds. Also, a new Jabir package capable of generating 322 atomic descriptors was designed and developed. Comparisons revealed the superiority of the atomic features generated by Jabir over those generated by such previous ones as the Magpie package. Furthermore, an innovative hybrid technique was developed as the feature selection method (Soraya package). In order to design and develop Jabir and Soraya packages, we applied novel ideas and innovative approaches, such as: (i) using new and diverse physical atomic features in the Jabir package and considering three different states (Elemental, Subscript, Fraction) in order to calculate the atomic features of each compound and (ii) using an innovative hybrid technique in Soraya package, removing features that are highly correlated with each other (removing redundant features) and using SHAP's technique to select the most important features and finally using the forward method to adding the most important features. The contributions of the study led to optimized evaluation values (R2, RMSE, MAE) of DataH, DataS, and DataK datasets without the need for any data pre-processing. The present study’s results indicate that the procedure of selecting the most important descriptors significantly impacts predicting superconducting materials’ Tc values. Finally, the development of a novel web application was a pioneering contribution to the field for predicting and determining the Tc of superconducting materials.