Construction of Li-ion conductivity prediction model for perovskite-type solid electrolyte using data from academic papers

AI technology for creating appropriate training data for machine learning from academic papers

Abstract

If the physical property values of materials can be evaluated by AI, it is expected that material search can be greatly accelerated. Aiming to explore new battery materials, we will create appropriate training data from academic papers, and construct an ionic conductivity prediction model. In this study, we constructed a Li-ion conductivity prediction model for perovskite-type solid electrolyte. In creating training data, we found the following three problems: (1) Errors in extracting data from papers, (2) Errors by the authors of the paper (measurement errors, incorrect citation of results in reference papers, etc.), (3) Mixing of data with different experimental conditions. (1) can be solved to some extent by improving our data extraction process. On the other hand, for (2) and (3), technology to detect inappropriate data is required. In this study, even when using the training data that was prepared with sufficient care for (1), the correlation coefficient between the predicted value and teacher data was only about 0.5. Therefore, we developed a technique to exclude inappropriate data using machine learning and succeeded in improving the correlation coefficient to 0.84.