[13p-A401-1~12] Science and applications of integrated two-dimensional materials

△ [13p-A401-8] Machine Learning Analysis of Valley Optical Properties in Two Dimensional Semiconductors Using Spatial Valley Polarization Heterogeneity

〇Kenya Tanaka¹, Kengo Hachiya¹, Wenjin Zhang¹, Kazunari Matsuda¹, Yuhei Miyauchi¹ (1.IAE, Kyoto Univ.)

Keywords:valleytronics, transition metal dichalcogenides, Machine Leaning

Valley information of excitons in monolayer transition metal dichalcogenides (TMDCs) can be mutually converted with the circular polarization information of light. This unique characteristic of monolayer TMDCs has attracted much attention recently because it enables us to use the exciton valley degrees of freedom for future optoelectronic applications called optovalleytronics. We demonstrate machine learning analysis of valley exciton properties in monolayer TMDCs to examine the valley exciton physics using spatial heterogeneity of the optical properties. Based on the variable importance obtained from the analysis of the learnt model, we will discuss the results of the machine learning analysis from the viewpoint of physics.