Optimization of femtosecond laser induced gas-phase plasma reaction using machine learning

2023 Fiscal Year Final Research Report

• Project/Area Number: 21K03521
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 14030:Applied plasma science-related
• Research Institution: Nagoya University
• Principal Investigator: Matsuda Akitaka 名古屋大学, 理学研究科, 講師 (10413999)
• Project Period (FY): 2021-04-01 – 2024-03-31
• Keywords: 強レーザー場 / レーザーフィラメント / レーザー誘起プラズマ / 機械学習

Outline of Final Research Achievements

Optimal control system for femtosecond laser induced plasma reaction was developed incorporating femtosecond laser pulse shaping technique. Among the various types of machine learning algorithms, genetic algorithm, which has been successful in selective breaking in intense laser fields, was adopted. The spectral phase of the femtosecond laser pulse was utilized as the genetic information. The developed system was applied to aerial femtosecond laser plasma and the optimal phase which gives the maximum optical emission intensity was explored. The intensity of the plasma emission increased stepwise along with the generation. After the 40th generation, the intensity exceeded that expected for the shortest pulse. This indicates that the laser plasma reaction was successfully optimized.

Free Research Field

光分子科学

Academic Significance and Societal Importance of the Research Achievements

気相プラズマ反応は高い反応性を示すことから，物質科学の側面からエネルギーや環境など多岐にわたる問題を解決するための反応法として注目を集めている。本研究では，フェムト秒強レーザー場において成功が収められてきたレーザーパルス波形整形による反応制御法をレーザー誘起気相プラズマ反応に適用し，発光強度を指標とした実験において機械学習による反応の最適制御が可能であることを明らかにした。今後，様々な分子系への適用を進めることで高効率・高選択的な新奇プラズマ反応法の開拓につながると期待される。