[P01-P39] Poster sess.

Japanese

[P38] Using First-Principles Calculations and Machine Learning for Exploring Alloy Catalysts to Decompose Organofluorine Compounds

○Yukino Yamamoto¹, Kosuke Harashima¹, Syogo Takasuka¹, Tomoaki Takayama¹, Mikiya Fujii¹ (1. Nara Institute of Science and Technology)

Keywords:

Fluorine-contained organics,First principles calculation,Machine learning

The aim of this study was to develop an alloy catalyst that promotes the decomposition of organofluorine compounds, and to establish a catalyst search method that combines first-principles calculations and machine learning. High-throughput first-principles calculations were used to comprehensively calculate the electronic states of the alloys, and to search for the optimal electronic states for the decomposition of organofluorine compounds. We also attempted to efficiently search for optimal experimental conditions by using machine learning.