English Abstract

Informatics Research in Functional Synthetic Rubber Materials

Takumi ADACHI *1 *2
Ken ITOMI *1 *2
Ryouta YAMAMOTO *1 *2
Shou KUBOUCHI *1 *2
Jun MORITA *1 *2
Hiroshi MORITA *4
*1:Research Association of High-Throughput Design and Development for Advanced Functional Materials (ADMAT), Tsukuba, Ibaraki, Japan
*2:JSR Corporation, Yokkaichi, Mie, Japan
*3:Nanomaterials Research Institute, Tsukuba, Ibaraki, Japan
*4:Research Center for Computational Design of Advanced Functional Materials, Tsukuba, Ibaraki, Japan
Nippon Gomu Kyokaishi,(2022),95(2),40-46 General Review in Japanese

The relationship between structures and physical properties for rubber blend was investigated by materials informatics approach. SBR/IR/Silica blend rubber, which is commonly used as tire rubber, was targeted in this paper. Model structures were created on a computer, and physical properties were calculated by finite element method (FEM) simulation. Datasets with structural features as explanatory variables and calculated values as objective variables were obtained by three-dimensional structure analysis for model structures. To clarify the contribution of each structural features to physical properties, machine learning was performed using the dataset with the modulus at 50% elongation (M50) as the objective variable. The prediction accuracy of the model was as high as 0.87 in R2 value. Furthermore, SHapley Additive exPlanation (SHAP) analysis revealed that the continuity of SBR phase and the shape of the phase separation interface are particularly important for physical characteristics. Finally, we created a structure-property correlation diagram that allow us to narrow down the compositions and structures that satisfied target physical properties without actual experiments. It follows that material development period will be shortened.

Keywords: Rubber Blend, FEM Simulation, Modulus, Machine Learning, SHAP Analysis, Structure-property Correlation Diagram