ML technology prioritizes choosing an appropriate dataset and then transferring it to a shape that an algorithm can handle. The Material Project is a database of materials and core program of the Materials Genome Initiative (Jain et al., 2013). It contains information about compounds from the Inorganic Crystal Structure Database (ICSD). Figure 1B gives the lithium ionic conductivity distribution of all Li-contained compounds by pymatgen (Ong et al., 2013). The data can be computed and analyzed by the Materials Application Programming Interface. The database includes information on the lattice structure, band structure, density of state, space group, energy, and phase diagram, etc. Different ML algorithms may use diverse descriptors and various representations as an input (Curtarolo et al., 2012; Jain et al., 2015; Kiran and Joseph, 2017). The more suitable the representation of the input data, the more accurately an algorithm can map it to the output data. For this reason, suitable input data should be easier to acquire than the output attributes that will be predicted, and it is better when the dimensionality is lower (Ghiringhelli et al., 2015).

To solve simulation errors, data should be preprocessed deliberatively. The preprocessing stage extracts raw data features and gives the subset unique characteristics, consisting of a continuous curve or discrete data. Feature extraction involves the transformation of the original data to essential characteristics. Its transforms, maps, or changes the dimensions of the original data into new data that reflects the input information more clearly. A large number of descriptors encode structures and properties, including: Coulomb Matrix (Rupp et al., 2012); Finger Prints; Graph Theory (Bonchev and Rouvray, 1991); 3D Geometry, Voronoi Tessellations (Ward et al., 2017); Simplified Molecular Input Line Entry Systems (SMILES) based on radial distribution functions (Schütt et al., 2014); and property-labeled material fragments (Isayev et al., 2017).

Neural networks, like the human brain, have many layers and units. Weights store knowledge and then form a completed net. It can process the input data layer by layer, thus converting the initial input representation into a more closely related representation to the output target. Learning is the process of adjusting the weights so that the training data is more accurate.

Sendek et al. reported a neural network for predicting the fast Li-ion conduction at room temperature (RT). They found that Li₅B₇S₁₃, Li₃InCl₆, and Li₂B₂S₅ had high ionic conductivity (Sendek et al., 2018). As shown in Figure 1C, 11 crystalline compounds were identified from 317 candidate materials by the ML-guide method. The ionic conductivity of Li₅B₇S₁₃ was predicted to be 0.074 S/cm, six times higher than the best-known material (Ding et al., 2009; Zhao and Daemen, 2012; Chou and Hwang, 2014; Cubuk and Kaxiras, 2014; Xiao et al., 2015). This study built a convolution neural network to train the atomistic structure, and five descriptors were used as input data. The ML-based simulation revealed a 44-times improvement in the log-average of conductivity compared to random guesswork and manual computation. However, the input data contained less information about properties, the number of materials screened was small, and the training precision was low.

Zeeshan et al. predicted that LiAuI₄ and Ba₃₈Na₅₈Li₂₆N were superionic conductors with ionic conductivity of 9.4 × 10^–4 and 10^–3 S/cm respectively through crystal graph convolutional neural network (CGCNN) (Xie and Grossman, 2018; Zeeshan et al., 2018). The 100 networks model was used to predict shear and bulk moduli as well as some mechanical properties. The accuracy of the screening results was further confirmed by measuring electronic conductivity and thermodynamic stability. Compared to the usual models, the CGCNN method was more general but required more data to train. This method significantly decreased the costs involved with first-principles calculations.

A support vector machine classifies various samples by different labels to find a partition hyperplane. However, there have many partition hyperplanes that can separate the training samples. The boundary should classify the input samples robustly and have the strongest generalization ability to the unseen examples. The Fujimura team employed the support vector regression (SVR) method to predict ionic conductivity (Fujimura et al., 2013). Figure 1D shows the predicted 72 compositions. This work indicated that Li₄GeO₄ had the highest ionic conductivity. They used the SVM method with a Gaussian kernel to predict the low-temperature conductivities of the compounds. The phase transition temperature (T _c), diffusivity at 1600 K (D₁₆₀₀), the average volume of the disordered structures (V _dis), and experimental temperature T were regarded as independent variables, while the logarithm of ionic conductivity as the dependent variable. To confirm which descriptor combinations can show the best performance, they undertook many experiments and included different numbers of variables to estimate the prediction error. The bootstrapping error was lowest when it chose a combination of these three: D₁₆₀₀, T.

Cubuk et al. discovered that LiN₅P₃O, Li₃Na₄O₃, LiPO₃, LiMg₃K₂O₄, LiNaMg₃O₅, Li₂K₃GaO₄, Li₅Na₂O₃, Li₄NaGaO₄, Li₂MgO₂, Li₅K₂O_3, and Li₅Na₂NO₂ satisfied all of the screening criteria, including stability, high Li conductivity, low cost and weight, and a large window of electrochemical stability (Cubuk et al., 2019). They used 30 elemental descriptors from 40 materials to train a linear SVM through the leave-one-out cross-validation (Sendek et al., 2016). The prediction accuracy is low because the selected 40 materials are out of Material Project or ICSD. In order to solve the trade-off between ML model accuracy and previously uncharacterized materials, they took the outputs of the structure model in Sendek’s research (Sendek et al., 2018) as the labels to train the generic descriptors model, named transfer learning (Pan and Yang, 2010). Through this transfer learning, a new generic model was trained using three datasets, including 40 data, Material Project data, and 21 of the 12,716 lithium-containing materials, with an accuracy of 87.5%, 92.0%, and 85.7%, respectively.

Because of these complex chemical systems, it is still difficult to completely calculate total conductivity by existing computing capacity. Most conductors have added polymers, such as plasticizers, which makes the calculations more complicated. To solve this complex interaction computing, Kan Hatakeyama-Sato’s team used a polymer database to train a gradient boosting model, which shows a 90% accuracy for training data and 81% accuracy for the testing data (Hatakeyama-Sato et al., 2018). As shown in Figure 1E, experiment data were similar to the predicted ionic conductivity. The analysis indicated that electronegativity and polarity of the monomer units were dominant variables in determining ionic conductivity.

The regression model studies the relationship between dependent variables (targets) and independent variables (predictors), used in predictive analysis, time series models, and finding causal relationships between variables. The kernel ridge regression and gradient boosting regression algorithm model were used to find the potential solid-state electrolytes LiOH, LiAuI₄, LiBH₄, Li₂WS₄, and Ba₃₈Na₅₈Li₂₆N, etc. (Zeeshan et al., 2018), through training the elastic tensor from the materials project database. An elastic tensor can assess the interface stability between Li anode and solid-state electrolytes. Sendek’s team related the crystallographic directions with the elastic tensor. They took DFT-computed information on 482 electrolyte materials as input data and set up an ‘rbf’ kernel ridge model with α and γ equal to 0.01, and two gradient boosting models with different max depth, minimum samples per leaf, and minimum samples split. This study predicted the elastic tensor of 548 materials with a cubic crystal structure. Although the training data was small, the regression model was useful. They found a relationship between material stiffness and other characteristics like mass density, the ratio of bond ionicity, volume per atom, and sub lattice electronegativity.

Another study employed logistic regression (LR) to identify superionic (σ ≥ 10^–4 S/cm) and non-superionic (σ < 10^–4 S/cm) structures (Sendek et al., 2016). Austin’s team found that these compounds (BaLiBS₃, Li₅B₇S₁₃, Sr₂LiCBr₃N₂, SrLi(BS₂)₃, LiErSe₂, Li₂B₂S₅, Li₃ErCl₆, Li₂HIO, Li₂GePbS₄, LiSO₃F, LiCl, LiSmS₂, Li₃InCl₆, CsLi₂BS₃, LiMgB₃(H₉N)₂, RbLiS, LiHoS₂, LiErS₂, LiHo₃Ge₂(O₄F)₂, KLiS) had superionic probability ≥50%, band gap ≥1 eV, upper bound oxidation potential ≥4 V, no transition metals, energy above the convex hull = 0 eV per atom. The dataset consisted of 40 materials with 20 diverse feature characteristics shown in Table 1. It is worth noting that the Li–B–S system was also selected using NN methods (Sendek et al., 2018). The regression illustrated how a multi-descriptor could build a model with better predictive power than a single feature alone. They used LR to build all possible models and then calculated the training misclassification rate (TMR) and the cross-validated misclassification rate (CVMR). As shown in Figure 2A, the 5-feature model had minimal CVMR, proving that the model had statistical significance. The X-randomization performance metric parameter was above the conventional threshold of 0.5 for the optimal LR model. Figure 2B shows the classification performance. The y-axis presents the conductivity higher than 10^–4 S/cm, and four materials were misclassified.

It is difficult and time-consuming to calculate the migration barriers and ion diffusion of the solid-state electrolytes, especially considering the huge material space. The unsupervised learning algorithm can discover the relationship between properties and X-ray diffraction (XRD) intensities have emerged in screening lithium solid-state electrolytes. Zhang et al. found that Li₈N₂Se, Li₆KBiO₆, and Li₅P₂N₅ (i.e., three new materials systems) had an ionic conductivity higher than 10^–2 S/cm (Zhang et al., 2019). Figure 1C shows the tree dendrogram generated using the agglomerative hierarchical clustering method. The highest result appears in groups Ⅴ and Ⅵ. These new materials comprise new structures, chemistries, and compositions that are significantly different from existing chemistries. Using clustering methods to classify modified X-ray diffraction (mXRD) can define every anion lattice and fully capture the anionic crystal structure information. The data contained 528 compounds, and three different models were generated to classify the results. The latter two models were set to evaluate the robustness of the clustering algorithm.

The unsupervised learning rising algorithm discovered a quantitative correlation between group and conductivity. The clustering model captured the physical dependence of fast solid-state Li-ion diffusion on anion lattice and gathered excellent materials together. The unsupervised method solves the problem created by scarce datasets, unlike supervised learning models, which need label training data. In unsupervised learning, the label of training data is unknown. It relies on learning non-labeled data to reveal intrinsic properties and laws of data, which form the basis of data analysis. The variances and errors of model parameters are rarely affected by the experimental method.