Abstract
Traditionally, the discovery of new materials has often depended on scholars’ computational and experimental experience. The traditional trial-and-error methods require many resources and computing time. Due to new materials’ properties becoming more complex, it is difficult to predict and identify new materials only by general knowledge and experience. Material prediction tools based on machine learning (ML) have been successfully applied to various materials fields; they are beneficial for modeling and accelerating the prediction process for materials that cannot be accurately predicted. However, the obstacles of disciplinary span led to many scholars in materials not having complete knowledge of data-driven materials science methods. This paper provides an overview of the general process of ML applied to materials prediction and uses solid-state electrolytes (SSE) as an example. Recent approaches and specific applications to ML in the materials field and the requirements for building ML models for predicting lithium SSE are reviewed. Finally, some current obstacles to applying ML in materials prediction and prospects are described with the expectation that more materials scholars will be aware of the application of ML in materials prediction.
Highlights
Materials science often focuses on the study of materials’ processing, properties and applications
They confirmed that the symmetry and order of the modified X-ray diffraction (mXRD)-encoded anion lattice of solid-state electrolytes (SSE) are closely related to the ionic conductivity, which led to the prediction of 16 new compounds with high lithium-ion conductivity, a few of which exceed 10−2 S/cm
In the case of Lithium-ion batteries (LIBs) SSE prediction, we can see that machine learning (ML) algorithms perform well: helping researchers extend datasets by text mining from the literatures [36,80], providing new tools for screening SSE with high mechanical properties or high ionic conductivity
Summary
Materials science often focuses on the study of materials’ processing, properties and applications. The ML applied in the material field mainly consist of following steps: data acquisition, feature engineering, model construction, analysis and the targeted injection of new data for optimization progress [9] and, form a complete and self-consistent system (Figure 1A), which can be continuously and adaptively improved and achieve the purpose of predicting new materials. The high ionic conductivity SSE often face various problems such as narrow chemical windows or poor mechanical properties Under such strict standards, many materials scholars have done a lot of works in various aspects, it is still a considerable challenge to design SSE that can be commercially applied [17]. Only very few SSE have been able to achieve room
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