[1Ep01-05] Development of Data Processing and Data Utilization in Surface Analysis: Surface Analysis Division's Session

[1Ep03] Materials search method using high-throughput experimental screening and machine learning models

*Shin Tajima¹ (1. Toyota Central R&D Labs., Inc.)

To improve properties of materials, the effect of additive elements was investigated using high-throughput (HT) experiments and materials informatics (MI) technique. In this study, we focused on the oxide ion conductors and the electrode materials for lithium-ion battery. At first, material libraries were synthesized by the ink-jet technique, and the properties were estimated using high-throughput measurement system. Appropriate additives were searched by machine learning models using composition-based explanatory variables and experimentally-obtained objective variables. Next, specimens contained with the additives were synthesized by solid-state reaction method, and then the properties were verified experimentally. The results suggest that the combination between the HT experiments and the MI technique is effective for searching additives under the limited conditions in this study.