[2Aa01-14] データ駆動表面科学研究部会「データ駆動アプローチ研究の最近の動向」

[2Aa14S] Development of prediction system on TOF-SIMS spectra of organic and polymer materials by machine learning

*keisuke kamochi¹, kei hasegawa², satoka aoyagi^1,2 (1. Faculty of Science and Technology, Seikei University, 2. Mateirals and Life Science, Seikei University)

The interpretation of time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra is generally difficult because mechanisms of secondary ion generation and fragmentation are complicated. The development of TOF-SIMS database is also difficult because the spectral pattern can be changed depending on the measurement and the sample conditions. In this study, we are aiming at developing a prediction system for unknown materials including organic and polymer materials by means of machine learning. We conducted a fundamental study with TOF-SIMS data which have been acquired in our laboratory.